diff options
Diffstat (limited to 'ELECTRIC_MACHINERY')
19 files changed, 8000 insertions, 0 deletions
diff --git a/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter10-checkpoint.ipynb b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter10-checkpoint.ipynb new file mode 100755 index 00000000..392a2ce0 --- /dev/null +++ b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter10-checkpoint.ipynb @@ -0,0 +1,364 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 10: Introduction to Power Electronics" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.5, Page number: 508" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from pylab import *\n", + "import numpy as np\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "w=2*pi*60 #frequency of voltage(Hz)\n", + "R=10 #ohm\n", + "C=0.01 #F\n", + "Vo=120*sqrt(2) #maximum voltage(V)\n", + "Nmax=800\n", + "tau=R*C #time constant(s)\n", + "\n", + "#Calculations:\n", + "# diode = 1 when rectifier bridge is conducting\n", + "\n", + "diode=1\n", + "t=[0]*801\n", + "vs=[0]*801\n", + "vrect=[0]*801\n", + "vR=[0]*801\n", + "iB=[0]*801\n", + "\n", + "t=[0]*801\n", + "for n in range(1,Nmax+2,1):\n", + " t[n-1] = (2.5*pi/w)*(n-1)/Nmax\n", + " vs[n-1]=Vo*math.cos(w*t[n-1])\n", + " vrect[n-1]=abs(vs[n-1])\n", + "#if the rectifier bridge is ON:\n", + " if diode==1:\n", + " vR[n-1]=vrect[n-1]\n", + " if (w*t[n-1])<=(pi/2):\n", + " iB[n-1]=vR[n-1]-Vo*C*w*math.sin(w*t[n-1])\n", + " elif (w*t[n-1])<=3*pi/2:\n", + " iB[n-1]=vR[n-1]/R+Vo*C*w*math.sin(w*t[n-1])\n", + " else:\n", + " iB[n-1]=vR[n-1]/R-Vo*C*w*math.sin(w*t[n-1])\n", + " if iB[n-1]<0:\n", + " diode=0\n", + " toff=t[n-1]\n", + " Voff=vrect[n-1]\n", + " else:\n", + " vR[n-1]=Voff*exp(-(t[n-1]-toff/tau))\n", + " iB[n-1]=0\n", + " if (vrect[n-1]-vR[n-1])>0:\n", + " diode=1\n", + "\n", + "\n", + "\n", + "#Results:\n", + "iR=(1/R)*np.array(vR)\n", + "plot(1000*np.array(t),vR)\n", + "xlabel('time [msec]')\n", + "ylabel('voltage [V]')\n", + "xlim(0,22)\n", + "ylim(0,180)\n", + "plot(1000*np.array(t),vrect,'--')\n", + "grid()\n", + "print \"The required plots are shown below:\"\n", + "show()\n", + "plot(1000*np.array(t),iR)\n", + "xlabel('time [msec]')\n", + "ylabel('source current [A]')\n", + "xlim(0 ,22)\n", + "ylim(-50,250) \n", + "plot(1000*np.array(t),1.5*np.array(iB),'--')\n", + "grid()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "The required plots are shown below:" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEPCAYAAACk43iMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOE6H/T8o9Q6nwzgdWAAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x7f054c060710>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEPCAYAAACgFqixAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcVOW+P/DPDHJTGLzglfFIsUvljrfK1MRSEz2paWk7\nPF4qtTK3sml32afE+pVHzZ3urZ3cW819DtVGy3YZSJaKph3FVFKzixkUgwkoilyEAeb5/cGeSQSG\nWcDMetbM5/168ZI1lzVfP+J8Wc9azzM6IYQAERF5NL3aBRARkfrYDIiIiM2AiIjYDIiICGwGREQE\nNgMiIoILmkF+fj5GjRqFqKgo9O/fH6tWrQIApKSkwGg0Ii4uDnFxcdi1a5ftOStWrEB4eDiioqKw\ne/duZ5dIROTxdM6eZ1BYWIji4mJERkaivLwcgwYNwvbt2/HPf/4TgYGBSEpKavD4Y8eOYeHChTh8\n+DAuXLiAESNG4LvvvoOPj48zyyQi8mhOPzLo2bMnIiMjAQABAQGIjo5GQUEBAKCpPpSeno6ZM2fC\ny8sLISEhiIiIQHZ2trPLJCLyaC49Z5CXl4ejR49i5MiRAIANGzZg4MCBSExMRElJCQCgoKAARqPR\n9hyj0QiTyeTKMomIPI7LmkF5eTkeeOABrFu3DoGBgXjyySdx7tw5nDlzBmFhYVi8eLGrSiEioht0\ncMWL1NTUYNq0afjtb3+LKVOmAACCg4Nt9y9YsADx8fEA6o8E8vPzbfeZTCb07du3wf68O3ujtrTW\nBZUTEbmPmJgY5OTkNHmf048MhBB45JFHEB4ejqVLl9puLyoqsn3//vvvIyIiAgCQkJCAtLQ01NbW\nwmQy4fTp0xg2bFiDfdaW1mLPj3sghOCXg1/Lli1TvQZ7X5PfnYwdZ3aoXodW8pLti3lpI6+vvvqq\n2fdqpx8ZHDp0CKmpqYiOjkZcXBwA4NVXX8U777yDkydPwmw2o1+/fti8eTMAYPDgwZg6dSqio6Oh\n1+uxceNGeHt7N9rvkD5DnF26W8nLy1O7hGZdqrwEACitLlW5kl/JnJeMmJcyMubl9GYwYsQIWCyW\nRrdPmDCh2ec8//zzeP755+3u1+BraHNtJIdXPn8FH373IeJD49UuhchjcQayh5gzZ47aJTRLCIEA\nnwBcq72mdik2MuclI+aljIx5OX3SmTPodDposGxqxtLMpfi3oH/D0juWtvxgImo1e++dPDLwEFlZ\nWWqX0CwB+Rq7zHnJiHkpI2NebAYkBZ1Op3YJRB5Ns83gD5/+Qe0SNGX06NFql9Csrv5dpbsgQOa8\nZMS8lJExL5dMOnOGM8Vn1C6B2smLd72odglEHk+zRwZXq6+qXYKmyDhGeT0hBCrMFWqXYSN7XrJh\nXsrImJdmm4FME5So7QrKCnDr+lvVLoPIY2n20tLQtaHI/V2u2qVQOymrLkPvNb1R/ny52qUQuS23\nvLSUw0TupZNPJ1yrvYZaCxcgJFKDZpvB/jn71S5BU2Qco7S6WHkR5eZyBPoEStPkZc5LRsxLGRnz\n0uzVRJE9ItUugdpJSlYKBgQPQJBfEEqrStHVv6vaJRF5HM0eGZAyMl7XbGUdw+wV0AsVNXJcUSRz\nXjJiXsrImJdmjwzIveigw5FHj6hdBpHH4pGBh5BxjNKKaxNpH/NSRsa82AxIClybiEhdmp1n8Ltd\nv0PCLQkYFzZO7XKojV7Y+wJu7XYrZsXMUrsUIrdmb56BZs8ZlFwrwS9lv6hdBrWDl8e8rHYJRB5P\ns8NEBl+DNNeka4GMY5Q3MteZpVmfSAt5yYR5KSNjXpptBkG+QWwGbmbT8U1I3p2sdhlEHkmzzYBH\nBsrIeF3zjYJ8g6RZgFALecmEeSkjY16abgayvHFQ+2CDJ1KPZpvBjMgZWHbXMrXL0AwZxyitLlZe\nRFl1Wf1yFJI0eJnzkhHzUkbGvDR7NRHXr3Efz332HIaFDMOwkGEorZKjGRB5Gs0eGZAyMo5RWlln\nIAf5BUGvk+NHUua8ZMS8lJExLzn+55HH0+l0CO0cipyFOWqXQuSR2Aw8hIxjlFYyToKXOS8ZMS9l\nZMyLzYCkoAPXJiJSk9ObQX5+PkaNGoWoqCj0798fq1atAgCUlJRg7NixiI6Oxvjx43HlyhXbc1as\nWIHw8HBERUVh9+7dTe631lKL8A3hUv5WKSMZxyitgjsGI8AnQO0yGpA5LxkxL2VkzMvpC9UVFhai\nuLgYkZGRKC8vx6BBg7B9+3Zs2rQJYWFhWLJkCdauXYvc3FysW7cOx44dw8KFC3H48GFcuHABI0aM\nwHfffQcfH59fi/7XYkv+r/ij5A8l8Pf2d+ZfgYjILdhbqM7pRwY9e/ZEZGT9R1QGBAQgOjoaBQUF\nyMjIwKxZ9atUJiYmIj09HQCQnp6OmTNnwsvLCyEhIYiIiEB2dnaT++bEM8fJOEbZlNKqUlTXVqtd\nhmbykgXzUkbGvFx6ziAvLw9Hjx7FiBEjUFxcjG7dugEAgoODUVRUBAAoKCiA0Wi0PcdoNMJkMjW5\nP65P5H4efO9B7Mvbp3YZRB7HZc2gvLwc06dPx7p162AwGNpln1y+wHEyjlE2Jcg3SIqJZ1rJSxbM\nSxkZ83LJDOSamhpMmzYNDz/8MKZMmQIA6N69Oy5evIjg4GAUFxejR48eAOqPBPLz823PNZlM6Nu3\nb6N9zpkzB5euXMKfv/4zBt00CLGxsbaArYdg3NbedpBvELIPZaPnxZ5S1MNtbmt5OysrC1u3bgUA\nhIaGwi7hZBaLRcyaNUssWbKkwe2LFi0Sr7/+uhBCiD/96U/iqaeeEkII8eWXX4ohQ4aImpoakZ+f\nL/r16yfMZnOD51rLzi/NFxXmCmf/FdzCvn371C6hWUXlReJq1VUhhBBJmUli1cFVKlckd14yYl7K\nqJWXvbd8px8ZHDp0CKmpqYiOjkZcXByA+ktHly9fjhkzZmDLli3o1asXtm3bBgAYPHgwpk6diujo\naOj1emzcuBHe3t5N7ttoMDZ5O2lL8qfJGBM6BrNjZyPIj+eBiNSg2c9A1mDZ1Iz/+OA/cPdNd2N2\n7Gxs/HIjTFdN/ChMIidwy89AJvei09XPQF4wZIHKlRB5Ji5H4SGsJ5VkZF21VCYy5yUj5qWMjHmx\nGZAUuDYRkbo0fc7g4+8/xic/fIK/JPxF7ZKoDZI+ScKIfxuB+wfer3YpRG7Nbc8ZCCGQeyVX7TKo\njf40/k9ql0Dk8TQ9TCTTZ+bKTsYxyqZYhAW/lP2idhmayUsWzEsZGfPSdDPgchTup6auBqHrQtUu\ng8jjaPqcwY+Xf8Td/3M3cn/HoSJ34vv/fFH6bCn8OvipXQqRW1F1CWtn4pGBe5JlsToiT6LpZtDV\nvyuOzz+udhmaIOMYpVVRRRHKzeW2bRnOBcmcl4yYlzIy5qXpZqDX6dGvcz+1y6A2+l3m77Dzu522\nbYOvgUcGRC6m6WZAjrMubyujG8cwb+5yM2osNSpVU0/mvGTEvJSRMS9NzzMg92FdmwgAtj+wXcVK\niDwTjww8hIxjlFZcm0j7mJcyMubFZkBS4NpEROrSfDNYsHMBMs5mqF2G9GQco7Tq3rE7Ovl0UruM\nBmTOS0bMSxkZ89L8OYOquioUVxSrXQa1wfqE9WqXQOTxNH9kYPDhxDNHyDhG2ZzKmkoUVRSpWoOW\n8pIB81JGxry03ww4C9ntfPz9x3gy40m1yyDyKGwGHkLGMcrmyLAchZbykgHzUkbGvNgMSDoyLEdB\n5Gk03wxmxczCf93zX2qXIT0ZxyitCssLUWGusG3L0OBlzktGzEsZGfPSfDMI8AlAkF+Q2mVQGzyR\n8QQyf8i0bcswTETkaTTfDMgxMo5RWt24NlFnv87o6t9VpWrqyZyXjJiXMjLmxWZAUrh+baJOPp1w\n+onTKlZD5HnYDDyEjGOUVlybSPuYlzIy5sVmQFLg2kRE6rL7Gchr1qxpcQcBAQFYsGBBuxbVkus/\nx7PWUot+a/vBtNTUYKiBtGPBzgW4f+D9GP+b8WqXQuTW7H0Gst1m0Lt3byxcuLDZHQsh8Pbbb+Ps\n2bNtr1KBG/9C/q/449IfLqGjd0eX1kFEpCX2mgGEHcnJyfbudugxc+fOFT169BCRkZG225YtWyZC\nQkJEbGysiI2NFRkZGbb7Xn31VTFw4EARGRkpPvnkkyb3eWPZPVb3EL+U/dJirZ5s3759apegyIWy\nC6K0qlS119daXmpjXsqolZe9t3y75wxWr17d7H2FhYUtPgYA5s6di8zMzAa36XQ6JCUl4cSJEzhx\n4gQmTJgAADh27Bh27NiBU6dOITMzEwsWLIDZbLa7f0COSUrUvpJ2J+HDbz9Uuwwij6HoBPLly5ex\nadMm3H333YiNjXXoOSNHjkSXLl0a3S6aOFRJT0/HzJkz4eXlhZCQEERERCA7O7vF12AzaJmM1zXb\nE+QbpOq/qdbyUhvzUkbGvFpsBpWVlXj33Xdx3333ISYmBsnJyXjhhRdgMpna9MIbNmzAwIEDkZiY\niJKSEgBAQUEBjEaj7TFGo9Gh1+GMVfcT5Mv1iYhcyW4zeOihhxAZGYn9+/djyZIlyM3NRZcuXTB6\n9Gh4eXm1+kWffPJJnDt3DmfOnEFYWBgWL16seB9z5sxBSkoKUlJScHfh3bDkWmz3ZWVlNbiOl9tZ\nWLt2rVT1XL+9Y9cOZH6W2eD+S99csjV45iX/NvNStu2qvLKysjBnzhzb+6Vd9k42xMTEiNtuu02s\nXbtWnD9/XgghRGhoqOKTFrm5uQ1OIF+voKBA3HrrrUIIIV566SWxevVq230TJ04UBw8ebPScFsqm\nJsh8gm/SO5PER99+1OC2N7LfEAt3LlSpIrnzkhHzUkZzJ5BzcnLw1ltv4dKlS4iPj8fIkSNRVlaG\nCxcu2O8wLSgq+vVTrN5//31EREQAABISEpCWloba2lqYTCacPn0aw4YNa9NrUT0ZxyitRBPnj3oH\n9kaAT4AK1dSTOS8ZMS9lZMzL7jyDG3355Zd49913sX37dhiNRnzxxRctPuehhx7C/v37cfHiRfTs\n2RPLly/Hvn37cPLkSZjNZvTr1w+bN29GSEgIAODVV19Famoq9Ho91qxZg/HjG09EsnutLGnOpHcm\nYeGQhZh06yS1SyFya62edNYci8WCzz//HHfddVebi2sNNgPlsrKypPxtBAAmvjMRjw95XKpmIHNe\nMmJeyqiVl733TrvDRH/961+bfpJeb2sEzT2GSAmuTUSkLrtHBjfffDNee+21JjuJtcO88MILOHPm\njFOLbO61rXZ8swMZZzOw6b5NLq2D2se8D+chMToRY24ao3YpRG7N3pFBB3tPHDVqFHbu3Gl35+PG\njWt9Ze3EW++NC+VtO6lN6tkyeYvaJRB5PLvNYOvWrS4qo22C/NSdraoFWhzT/ab4GwwIHqDKarRa\nzEtNzEsZGfNyi88zMPgaOFvVDQ3921CUmcvULoPII7hNM+CRgX2y/RbiiCA/9ZYZ0WJeamJeysiY\nl0PN4Mcff3ToNrWovagZOQfXJyJyHYeawbRp0xrdNn369HYvprW6+HfBD0/9oHYZUrt+7RLZXCi/\ngMqayka3q3lkIHNeMmJeysiYl90TyN988w3OnDmD0tJS7NixA0II6HQ6VFRUoKxMnrFcvU6PLv6N\nl8kmbZj9z9lIuj2p0cde8siAyHXsNoPvv/8eO3fuRGlpaYNLTP39/bFpE6/p1xIZxyitmrvueWDw\nQHjpWr86blvInJeMmJcyMubl0HIUX3zxBYYPH+6KehzC5Sjcy7j/HYfk4ckYF6b+nBUid9bqSWdW\nN910E1JSUpCfnw+LxWLb6ZYtnCykFTJe12wlIF9jlzkvGTEvZWTMy6FmkJCQgHHjxmH8+PHQ6+vP\nOasxEYjcF9cmIlKXQ8NEcXFxOHHihCvqcUhThzqJOxLxQPgDmDxgskpVUWvN+mAW5g+aj5H9Rqpd\nCpFba/WqpVYTJ05EZmZmyw9UkV6nx5WqK2qXQa3wv1P/l42ASGUONYO1a9ciISEBfn5+CAwMRGBg\nIAwGg7NrU4SXIdon43XNLSk3l+NcyTlVXluLeamJeSkjY14ONYPy8nJYLBZUVVWhrKwMZWVluHpV\nrhm/XJLC/RwtOIpHdz6qdhlEHsGhZlBbW4tNmzZh2bJlAACTyYTs7GynFqYUm4F9sl254AiDr4Fr\nE2kE81JGxrwcagbz58/H8ePHkZaWBgAwGAxYuHChUwtTis3A/XA1WiLXcagZHDlyBG+88Qb8/f0B\n1DcD63wDWcyLm4d1965TuwxpyThGafVL2S+4VnOt0e1qfk6FzHnJiHkpI2NeDjWDDh06oK6uzrZ9\n+fJl1NbWOq2o1vDt4AvfDr5ql0Gt8Nsdv8Vh0+FGtwf51i9Ux9nmRM7nUDNYtGgRJk+ejKKiIrz4\n4ou444478PTTTzu7NmpHMo5RWjX3Zu/bwRcxvWJQY6lxcUVy5yUj5qWMjHm1OAPZYrEgIiICw4YN\nw6effgoASEtLQ0xMjNOLI8/R3Iz2o48ddXElRJ6pxSMDvV6PxYsXIyYmBsnJyUhOTmYj0CAZxyit\nZF2biBzHvJSRMS+HholGjx6NDz74gGO35DRcm4hIXQ6tTRQQEIDKykp4eXnBz8+v/ok6nWoTz5pa\nX6OmrgbdVnVD6bOlXERPY2a8NwNLb1+K2423q10KkVuztzZRi83AYrHg8OHDmvg8A/9X/HHpD5fQ\n0bujClUREcmtTQvVWc8ZaAEnnjVPxjFKR/xc+jOKKopc/rpazUstzEsZGfNy+jmDefPmoWfPnoiK\nirLdVlJSgrFjxyI6Ohrjx4/HlSu/rja6YsUKhIeHIyoqCrt371b0WkG+6k1SIudYfWg10k6nqV0G\nkdtzqBm8+eabmDZtGnx8fBSvWjp37txGy18vW7YMEydOxMmTJzFhwgTbmkfHjh3Djh07cOrUKWRm\nZmLBggUwm80O/2V4ZNA8Ga9rdkSQnzqr0Wo1L7UwL2VkzEvRqqU1NTWKVy0dOXIkunTp0uC2jIwM\nzJo1CwCQmJiI9PR0AEB6ejpmzpwJLy8vhISEICIiQtGCeGwG7of/pkSu4VAzOHDgQJNfrVVcXIxu\n3boBAIKDg1FUVD8mXFBQAKPRaHuc0WiEyWRyeL8ZD2cgPjS+1XW5MxnHKK3Ol51HVW1Vk/dZl6Rw\nNZnzkhHzUkbGvBz6DORVq1bZLtesqqpCdnY2Bg8ejL179zq1OHvmzJmD0NBQAEDnzp0RGxtrO/Sy\nBs3tX7dzcnKkquf67XEvj8OCwQvw1IynGt1v8DXg7PGzyArMcml9Mucl4zbzkjOvrKwsbN26FQBs\n75fNEq1gMpnEtGnTHH58bm6uiIyMtG3ffPPNori4WAghRFFRkQgLCxNCCPHSSy+J1atX2x43ceJE\ncfDgwUb7a2XZJKk7Nt0hDv7U+N9ZCCEO5B0QSZlJLq6IyD3Ze+90aJjoRn369MHJkydb81QAQEJC\nAlJTUwEAqampSEhIsN2elpaG2tpamEwmnD59GsOGDWv165A2CIhmJwqO7DcSa8avcXFFRJ7HoWGi\np556yva9xWJBTk6Ow+sTPfTQQ9i/fz8uXryIvn374qWXXsLy5csxY8YMbNmyBb169cK2bdsAAIMH\nD8bUqVMRHR0NvV6PjRs3wtvbuxV/LbpRVtavwywykm05Ctnzkg3zUkbGvBxqBoMHD7b95qbX6zF9\n+nSH/yLvvvtuk7dbV0C90fPPP4/nn3/eoX03RYjmf8skOQmueUWkOofWJiovL4e/vz+8vLwAAHV1\ndaiurkbHjuos+9DclOq002n46PuP8Pb9b6tQFbXW1LSpeHHUi4jrHad2KURurU3LUQDAmDFjGkz+\nqqqqwpgxY9qnunbUyacTrlRdafmBJJUPZnzARkCkMoeagdlstn3+MQB06tQJVVVNXxeuJk5Qap71\ncjMt+vL8l6ipc+2nnWk5LzUwL2VkzMvhz0D+6quvbNs5OTnQ61t1IZJTsRm4p8n/mIzCikK1yyBy\naw6dQF63bh0mTpxom7SQl5eHtDT5Fg9jM2iebFcuKKHGAoRazksNzEsZGfNyqBnceeedOHfuHE6e\nPAmdToeoqCj4+vo6uzbF2Azck8HXoMqSFESexOGxHl9fXwwdOhRDhgyRshEAQDf/bihM5nBCU2Qc\no7QquFqA6trqZu8P8nP9kYHMecmIeSkjY17yDfy3gU6nQwe9Qwc7JJHJ/5iMU0Wnmr3f4GtQZRlr\nIk/iVs2AmifjGKWVgP2pLjE9Y9DJu5OLqqknc14yYl7KyJiXQ79G19bWYuvWrcjPz8fy5cthMplw\n/vx5rhtE7cbechT/Oeo/XVgJkWdy6Mhg/vz5OH78uO0KIoPBgIULFzq1MGpfMo5RWsm4HIXMecmI\neSkjY14OHRkcOXIEX3/9NeLi6meJGgwGWCwWpxbWWkIICAjodRwB0xKuJ0WkLocnndXV1dm2L1++\njNraWqcV1RZT06Zi53c71S5DOjKOUVr1CewDHy8ftctoQOa8ZMS8lJExL4eODBYtWoTJkyejqKgI\nL774IrZt24bnnnvO2bW1SoBPAOcaaMzHv/1Y7RKIPJ5DRwaPPfYYXnnlFSxduhQGgwFpaWmYPXu2\ns2trFU48a5qMY5SOulp9FTkXclz6mlrOSw3MSxkZ83KoGfzf//0fbr75ZiQnJyM5ORlhYWE4fPiw\ns2trFTYD9/NN8TeYv3O+2mUQuTWHmsHChQsRGBho2+7YsSMef/xxpxXVFmwGTZNxjNJRavybajkv\nNTAvZWTMy6FmcOOVQ3q9XtoTyAZfA8rN5WqXQe0oyC+IM5CJnMyhZhASEoINGzagpqYGZrMZ69ev\nR58+fZxdW6s8MfQJ/CXhL2qXIR0ZxyitTFdNMNeZm71fjSMDmfOSEfNSRsa8HGoGW7duxe7du9Gt\nWzd0794de/bswd///ndn19YqnF+gPQlvJ+Dbi982e38n706orq12+QfcEHmSFi8traurw9NPP40P\nP/zQFfWQk8g4RmnV0tpEOp0O48LGobquGt5e3i6pSea8ZMS8lJExrxabgZeXF/Lz81FbW4sOHbgi\nKDmHvbWJACDj4QwXVULkmRx6d+/bty/uuOMO3HfffejYsSOA+t/WkpKSnFoctZ+srCwpfxsB5F2b\nSNa8ZMS8lJExL4eaQVhYGMLCwmCxWFBeXg4hhNRrydRaavm5Bhoj888TkSfQCRl/LWuBTqdr9rfJ\nyppKBK8KRuUfK11cFbXW+NTx+PO9f0b/4P5ql0Lk1uy9dzr063N8fHyTO927d2/bKnMC/w7+MNeZ\nUVNX47KTjdQ2nyR+onYJRB7PoWawevVq2/dVVVX44IMPoNfLeQmnTqeDwdeAMnMZuvp3Vbscacg4\nRqnEDyU/wFvvjX6d+7nk9bSel6sxL2VkzMuhZjBkyJAG2yNGjMDtt9/ulILag3WSEpuB+/jbsb+h\ni38XPDviWbVLIXJLDjWDkpIS2/cWiwVffvklCgsL2/zioaGhMBgM8PLygre3N7Kzs1FSUoIZM2ag\nsLAQvXv3RlpaGjp37qxov1yfqDHZfgtRyuBrQGmV65ak0Hpersa8lJExL4eawaBBg2xXe+j1ehiN\nRmzevLnNL67T6ZCVlYWuXX/9DX7ZsmWYOHEilixZgrVr12LZsmVYt26dov129uvM9YncTJBfEM6X\nnVe7DCK35dDAf15eHnJzc5Gbm4tz585h//79GDNmTLsUcOOZ7YyMDMyaNQsAkJiYiPT0dMX73D9n\nP4b3Hd4u9bkLGddCsWppbSLgX0cGLlysTua8ZMS8lJExL4eaQXV1NVauXIlJkyZh0qRJWL16Ncxm\n+/95HaHT6TB27FhER0dj/fr1AIDi4mJ069YNABAcHIyioqJW7Ze0457/uQc/Xv7R7mOCfIM49Efk\nRA4NE82bNw++vr5ISkqCEALvvvsu5s6di7fffrtNL3748GH06NEDxcXFuPfeezFgwACHnztnzhyE\nhoYCADp37ozY2FjbOJy163K74baVLPVYtyvOViD7UDYGTB7Q7OMLLxWif7f+Lq3PSu18tLJtJUs9\nsm9bOfP1srKysHXrVgCwvV82x6FJZxEREfj6669bvK0tVqxYAQDYtGkTjhw5guDgYBQXF+OOO+7A\nDz/80LBoOxMnSHv6r++Pj2Z+xElnRE5m773ToWEivV6PvLw823ZeXl6b5xlUVlaisrJ+lnBFRQUy\nMzMRERGBhIQEpKamAgBSU1ORkJDQptehejf+NiITGRu7zHnJiHkpI2NeDg0TrVy5Erfffjv696//\nze37779v89VEhYWFmDJlCnQ6HSorKzFz5kzcd999GDFiBGbMmIEtW7agV69e2LZtm+J9W4QFNXU1\n8O3g26YayXV4nodIXQ6vTVRZWYnTp09Dp9MhKioKfn5+zq6tWS0NE7114i18/vPn2DJ5iwurotYa\n8/cx2HTfJtzc5Wa1SyFya20eJtq2bRssFguGDRuGXbt24cEHH0R2dna7FtmeOOlMW/bO3stGQKQy\nh5rByy+/jICAABw4cAD79u3D/PnzsWjRImfX1mpsBo3JOEap1P68/bhWc80lr+UOebkS81JGxrwc\nPoEM1E8Ie/TRRzFp0iTU1tY6tbC2YDNwT4/ufBT5V/PVLoPILTnUDEJCQvDEE09g+/btmDhxIsxm\nM5uBxlivQdayIN8gl61P5A55uRLzUkbGvBxqBv/4xz8wevRoZGZmonPnzigpKcFrr73m7NpazeBr\nQI2lRu0yqJ2xyRM5j0PNwGAw4MEHH8Qtt9wCAOjVqxfGjRvn1MLaIsQQgrNPnVW7DKnIOEZplV+a\nj5q6lpt3kJ/rlqSQOS8ZMS9lZMxLzk+oIY9y19a7HDoX4OrF6og8CZuBh5BxjNJKwLEZyEP7DEVw\nx2AnV1NP5rxkxLyUkTEvh2YgEzmbDi3PQF40TN7LmYm0jkcGHkLGMUorrk2kfcxLGRnzcttmUFVb\n5dBJSZID1yYiUpfbNoPJ/5iMvbl71S5DGjKOUVoZDUZ00Ms1YilzXjJiXsrImJdc/wPbUaBPIK9J\n14iD8w7OU7GOAAAOOElEQVSqXQKRx3PbIwNOUGpIxjFKpUqrSnHgpwMueS13yMuVmJcyMubFZkCa\n8XPpz3gi/Qm1yyByS2wGHkLGMUqlXDkD2R3yciXmpYyMebltM+jq3xW1FnkX0yPlOAOZyHncthks\nuX0JXh7zstplSEPGMUqrn0t/dqhxB/oEotxcDouwOL0mmfOSEfNSRsa83PZqItKO4ZuH4/Cjh2E0\nGO0+zkvvhY7eHfHJvnJ8sc/Q6P4bpyo0NXWhpcdYt3NzgQMH2n+/zqpX7f2ePQucOqWdetXe7zff\nAAUFyp4zYwagd+Kv7w5/BrJMdDodXnhBwMcH8PaG7U9fX8DPr/kvLy/n1+aqNN3pde7JCEHq6CMI\nFEaUlQFlZcDVq/V/VlQAdXX1XxYLsEPMxsXU1/Hw/V1huK4f3FhnU3W3x2O4X/eoRYv7ffvttjcD\ne5+BrNlmkJIiYDYDNTWw/VldXf9VVdX469q1+jcT19TH11Hi24khCNuTjSB9CAIDgcBAwGCo/7Nj\nR6BDh/pG7uVV/58hJAR45BHn1kTkjtyyGWiwbFVlZWVJeQUDAPRZ0wdHHzuKEEOI2qXYyJyXjJiX\nMmrlZe+9021PIFuEBZevXVa7DHIQ1yYiUpfbHhmUXCtB2J/DcPkZNgTZ3bbpNnw08yP0DOipdilE\nbs3ee6fbXk1kXZtICMHfOiV35NEjapdA5PHcdpjI28sbvl6+qKypVLsUKch4XXNrfFP8Db69+K3T\nX8dd8nIV5qWMjHm5bTMAuCSFO9p+ZjveOfWO2mUQuR02Aw/hLld6GHwNKK1y/pIU7pKXqzAvZWTM\nS8pmkJmZiaioKISHh2PlypWt3k+IIQTXaq+1Y2WkNoOvAVfNbPBE7U26ZlBdXY3HH38cmZmZOHny\nJN577z2cOHGiVfvaN3sfYnvFtnOF2iTjGKXVT1d+Qp2lzqHHBvkGueTIQOa8ZMS8lJExL+mawZEj\nRxAREYGQkBB06NABM2bMQHp6utplkRMN/dtQXLp2yaHHcuiPyDmkawYmkwl9+/a1bRuNRphMJhUr\ncg8yjlG2Rt+gvhjaZ6jTX8dd8nIV5qWMjHlJN8+gvecEfH/pe9zS9ZZG+5374Vx88sMnjR6/ZfIW\n3Pubexvdzsc77/El10rg6+Xb6DFNGRA8ACvuWYG9uXuRuCOx0f3xN8Xj7fvfbnQ7H8/Hu9PjnUG6\nGciff/45Vq5ciY8//hgAsHr1apjNZvzxj3+0PUan02H27NkIDQ0FAHTu3BmxsbG2bmsdj7tjxB0w\n/JcBmXdmQqfTNbj/avVVDBk+BADwxedfAACGjxyOLn5dcORQ/SQod3r8oexDmPv4XGnquf7xRw8d\nRZBfUKN/P3vb5jozIodFNno9Xy9fnMo+1ebHHz12FA/Pf9hp+3e3x3998ms89uRj0tQj++NzcnKw\nZMmSVu/f0e2srCxs3boVABAaGorly5drZ6G6qqoqDBgwAIcOHUKPHj0wfPhwbNy4EYMGDbI9xtGF\n6r6/9D2G/m0oSp/lp2NxITFlmJcyzEsZGReqk64ZAMCuXbvw9NNPw2KxYNasWXjuueca3O9oM5jz\nzzn4+1d/h1gm3V+RiMjlNLc20YQJEzBhwoQ278fRcWgiIk8n3dVE7WnV2FU488QZtcuQgozXNcuM\neSnDvJSRMS8pjwzaS5BfEIL8gtQug4hIelKeM2gJP+mMiEg5j/ykMyIichybgYeQcYxSZsxLGeal\njIx5sRkQERHPGRAReQqeMyAiIrvYDDyEjGOUMmNeyjAvZWTMi82AiIh4zoCIyFPwnAEREdnFZuAh\nZByjlBnzUoZ5KSNjXmwGRETEcwZERJ6C5wyIiMguNgMPIeMYpcyYlzLMSxkZ82IzICIinjMgIvIU\nPGdARER2sRl4CBnHKGXGvJRhXsrImBebARER8ZwBEZGn4DkDIiKyi83AQ8g4Rikz5qUM81JGxrzY\nDIiIiOcMiIg8Bc8ZEBGRXao0g5SUFBiNRsTFxSEuLg67du2y3bdixQqEh4cjKioKu3fvVqM8tyTj\nGKXMmJcyzEsZGfNSpRnodDokJSXhxIkTOHHiBCZMmAAAOHbsGHbs2IFTp04hMzMTCxYsgNlsVqNE\nt5OTk6N2CZrCvJRhXsrImJdqw0RNjVulp6dj5syZ8PLyQkhICCIiIpCdna1Cde7nypUrapegKcxL\nGealjIx5qdYMNmzYgIEDByIxMRElJSUAgIKCAhiNRttjjEYjTCaTWiUSEXkMpzWDsWPHIioqqtHX\nRx99hCeffBLnzp3DmTNnEBYWhsWLFzurDPqXvLw8tUvQFOalDPNSRsq8hMoKCgrErbfeKoQQ4qWX\nXhKrV6+23Tdx4kRx8ODBRs8JCwsTAPjFL37xi18KvmJiYpp9L+4AFRQVFaFHjx4AgPfffx8REREA\ngISEBCxcuBBLlizBhQsXcPr0aQwbNqzR83/44QeX1ktE5O5UaQa///3vcfLkSZjNZvTr1w+bN28G\nAAwePBhTp05FdHQ09Ho9Nm7cCG9vbzVKJCLyKJqcgUxERO1LczOQMzMzERUVhfDwcKxcuVLtcqQX\nGhqK6OhoxMXFNTnk5unmzZuHnj17IioqynZbSUkJxo4di+joaIwfP17KywDV0lReN04izczMVLFC\nueTn52PUqFGIiopC//79sWrVKgCS/oy19wlhZ6qqqhKhoaHCZDKJmpoaMWTIEHH8+HG1y5JaaGio\nuHTpktplSOvAgQPi+PHjIjIy0nbbokWLxOuvvy6EEOL1118XixcvVqs86TSVV0pKilizZo2KVcnr\nwoUL4tSpU0IIIcrKysQtt9wicnJypPwZ09SRwZEjRxAREYGQkBB06NABM2bMQHp6utplSU9wJLBZ\nI0eORJcuXRrclpGRgVmzZgEAEhMT+TN2nabyAvgz1pyePXsiMjISABAQEIDo6GgUFBRI+TOmqWZg\nMpnQt29f2zYnpbVMp9PZDkfXr1+vdjmaUFxcjG7dugEAgoODUVRUpHJF8mtqEik1lJeXh6NHj2LE\niBFS/oxpqhnodDq1S9Ccw4cP4/jx49izZw/eeustfPbZZ2qXRG6Gk0hbVl5ejunTp2PdunUwGAxq\nl9MkTTUDo9GI/Px823Z+fn6DIwVqzDqfo3v37pg+fTqOHj2qckXy6969Oy5evAig/ijBmiE1LTg4\nGDqdDjqdDgsWLODP2A1qamowbdo0PPzww5gyZQoAOX/GNNUMhg4ditOnT6OgoAA1NTXYtm2bbcVT\naqyyshKVlZUAgIqKCmRmZtom+FHzEhISkJqaCgBITU1FQkKCyhXJ7fohjusnkVL9uZRHHnkE4eHh\nWLp0qe12KX/GVD6BrVhGRoaIiIgQAwcOFK+++qra5Ujtxx9/FNHR0SImJkbccsst4oUXXlC7JOnM\nnDlT9O7dW3h7ewuj0Si2bNkiLl26JO655x4RFRUlxo4dKy5fvqx2mdK4Ma/NmzeLxMREER0dLQYM\nGCDGjx8vTCaT2mVK4/PPPxc6nU7ExMSI2NhYERsbK3bt2iXlzxgnnRERkbaGiYiIyDnYDIiIiM2A\niIjYDIiICGwGREQENgMiIgKbARERgc2A3FhpaSn++7//27Z9/vx5PPDAA+3+Otb1/FNSUtp93y2J\nj49HYGAgjh075vLXJvfCZkBu6/Lly3jjjTds23369MH27dvb/XV0Oh2SkpJUaQb79u3DkCFDuIgj\ntRmbAbmtZ599FufOnUNcXByeeeYZ/PTTT7ZP6Nq6dSumTJmCCRMm4KabbsL69evx2muvYciQIRg0\naJBtEbHvvvsO8fHxiImJwW233Yavv/66yde6fiJ/SkoKZs+ejfj4eISGhmLHjh1ITk5GdHQ07r77\nblRXVwMAnn76aURERCA2NhZJSUkAgAsXLmDSpEmIiYlBbGws9u/fDwAoKyvDzJkzERERgZiYGLz3\n3ntOy408lMrLYRA5TV5eXoNP5MrNzbVtv/XWW+I3v/mNuHbtmiguLhYGg0Fs2rRJCCHE0qVLxerV\nq4UQQgwfPlycPXtWCCHE4cOHxZ133tnodVJSUsRrr71m2162bJkYNWqUsFgs4quvvhL+/v5i9+7d\nQgghpk6dKrZv3y4KCwtFRESE7Tnl5eW2+w8ePCiEEOKnn34SYWFhQgghFi9eLJKTk22PLy0ttX0/\nevRocezYsdbGRCSEEKKD2s2IyFlEC8tuxcfHw8/PD35+fujcubNt5cioqCjk5OTg0qVLOH78eIPz\nDNeuXWvxdXU6He69917odDpERkbCYrFg7Nixtn3n5+ejW7du8Pb2xiOPPIKEhAT8+7//OwDgs88+\nQ25urm1f1dXVuHr1Kvbs2YMPP/zQdrusa+KTdrEZkMfy9fW1fa/X623ber0eFosFQgh0794dJ06c\nULxvHx8f2768vb0bvI7FYoGXlxeOHDmCPXv24P3338eGDRuwd+9e6HQ6HD16FB06NP6v2VJzI2oL\nnjMgt+Xv72/7PAclrG+6wcHB6N69Oz7++GPb7c2dM1CqoqICZWVlmDBhAtasWYPjx48DAO655x68\n+eabtsdZX2/s2LHYuHGj7farV6+2Sx1EVmwG5LZ69uyJ2NhYhIeH45lnnrF9GheABt9bt6//3rqd\nlpaGNWvWIDo6GpGRkQ6fuG1u39btq1ev4t5770VcXBxGjhyJ119/HQDw5ptv4tNPP0VUVBQiIyOx\nbt06AMDLL7+Mn3/+GeHh4YiNjcWePXtakQhR8/h5BkRttHz5cgQEBOD3v/+9Kq8fHx+PNWvWYNCg\nQaq8PrkHHhkQtVFAQAD++te/qjbpLDc3t8F5CaLW4JEBERHxyICIiNgMiIgIbAZERAQ2AyIiApsB\nEREB+P84YbaUtpWtuAAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x2986d90>" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.6, Page number: 522" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "w=2*pi*60 #Angular freq of voltage(rad/sec)\n", + "Vo=230*sqrt(2) #volt\n", + "R=5.6 #Resistance(ohm)\n", + "\n", + "#Calculations:\n", + "Ls=[0]*101\n", + "tc=[0]*101\n", + "Idc=[0]*101\n", + "for n in range(1,101,1):\n", + " Ls[n-1]=n*10**-3\n", + " Idc[n-1]=2*Vo/(pi*R+2*w*Ls[n-1])\n", + " tc[n-1]=(1/w)*acos(1-(2*Idc[n-1]*w*Ls[n-1])/Vo)\n", + "\n", + "#Results:\n", + "plot(1000*np.array(Ls),Idc,'g.')\n", + "xlabel('Commutating inductance Ls [mH]')\n", + "ylabel('Idc [A]')\n", + "title('Load current,Idc vs Commutating inductance,Ls')\n", + "show()\n", + "plot(1000*np.array(Ls),1000*np.array(tc),'g.')\n", + "xlabel('Commutating inductance L [mH]')\n", + "ylabel('tc [msec]')\n", + "title('Commutating Inductance,Ls vs time,tc')\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['fmod', 'sinh', 'trunc', 'tan', 'gamma', 'cosh', 'radians', 'modf', 'expm1', 'ldexp', 'linalg', 'random', 'frexp', 'ceil', 'isnan', 'copysign', 'cos', 'degrees', 'tanh', 'fabs', 'sqrt', 'hypot', 'power', 'log', 'log10', 'info', 'log1p', 'floor', 'fft', 'pi', 'exp', 'isinf', 'e', 'sin']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEZCAYAAACXRVJOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x2c18b90>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEZCAYAAACQK04eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x2981fd0>" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.7, Page number: 528" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "R=12.5*10**-3 #ohm\n", + "L=1.2 #H\n", + "Vo=15 #volt\n", + "w=120*pi #angular freq(Hz)\n", + "Idc=35 #DC current(A)\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "theta=[0]*1301\n", + "t=[0]*1301\n", + "vL=[0]*1301\n", + "vs=[0]*1301\n", + "\n", + "Vdc_a=R*Idc #Dc voltage(V)\n", + "P=Vdc_a*Idc #Power\n", + "alpha_da = acos(pi*R*Idc/(2*Vo)) ; #delay angle\n", + "for n in range(1,1301,1): #loop for calculating load voltage\n", + " theta[n-1]=2*pi*(n-1)/1000\n", + " t[n-1]=theta[n-1]/w\n", + " vs[n-1]=Vo*sin(theta[n-1])\n", + " if theta[n-1]<alpha_da:\n", + " vL[n-1]=-vs[n-1]\n", + " elif (theta[n-1]<pi+alpha_da):\n", + " vL[n-1]=-vs[n-1]\n", + " elif theta[n-1]<2*pi+alpha_da:\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<3*pi+alpha_da:\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<4*pi+alpha_da:\n", + " vL[n-1]=-vs[n-1]\n", + " else:\n", + " vL[n-1]=vs[n-1]\n", + "\n", + "figure(1)\n", + "plot(1000*np.array(t),vL,'g.')\n", + "xlabel('time [msec]')\n", + "ylabel('Load voltage [V]')\n", + "grid()\n", + "show()\n", + "\n", + "\n", + "#part(b):\n", + "alpha_db=0.9*pi #delay angle\n", + "Vdc_b=(2*Vo/pi)*cos(alpha_db) #new dc voltage(V)\n", + "tau=L/R #time constant(s)\n", + "imo=Idc #Initial curent(A)\n", + "tzero=-tau*log((-Vdc_b/R)/(imo-Vdc_b/R))\n", + "for n in range(1,1301,1):\n", + " theta[n-1]=2*pi*(n-1)/1000\n", + " t[n-1]=theta[n-1]/w\n", + " vs[n-1]=Vo*sin(theta[n-1])\n", + " if theta< alpha_db:\n", + " vL[n-1]=-vs[n-1]\n", + " elif (theta[n-1]<pi+alpha_db):\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<2*pi+alpha_db:\n", + " vL[n-1]=-vs[n-1]\n", + " elif theta[n-1]<3*pi+alpha_db:\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<4*pi+alpha_db:\n", + " vL[n-1]=-vs[n-1]\n", + " else:\n", + " vL[n-1]=vs[n-1]\n", + "\n", + "#Results:\n", + "figure(2)\n", + "plot (1000*np.array(t), vL,'g.')\n", + "xlabel('time [msec] ')\n", + "ylabel('Load voltage [V]')\n", + "print \"part (a):\"\n", + "print \"\\n Vdc_a=\",round(1000*Vdc_a,2),\"mV\"\n", + "print \"\\n Power=\",round(P),\"W\" \n", + "print \"\\n alpha_d=\",round((180/pi)*alpha_da,1),\"degrees\"\n", + "print \"\\n part (b):\"\n", + "print \"\\n alpha_d=\",round((180/pi)*alpha_db,1),\"degrees\" \n", + "print \"\\n Vdc_b=\",round(Vdc_b,1),\"V\"\n", + "print \"\\n Current will reach zero at\",round(tzero,1),\"sec\"\n", + "grid()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['fmod', 'cosh', 'sinh', 'trunc', 'tan', 'gamma', 'degrees', 'radians', 'sin', 'expm1', 'ldexp', 'isnan', 'frexp', 'ceil', 'copysign', 'cos', 'tanh', 'fabs', 'sqrt', 'hypot', 'log', 'log10', 'pi', 'log1p', 'floor', 'modf', 'exp', 'isinf', 'e']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhNj0/wELMsFZawmOEgAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x1e16bd0>" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "part (a):\n", + "\n", + " Vdc_a= 437.5 mV\n", + "\n", + " Power= 15.0 W\n", + "\n", + " alpha_d= 87.4 degrees\n", + "\n", + " part (b):\n", + "\n", + " alpha_d= 162.0 degrees\n", + "\n", + " Vdc_b= -9.1 V\n", + "\n", + " Current will reach zero at 4.5 sec\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter3-checkpoint.ipynb b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter3-checkpoint.ipynb new file mode 100755 index 00000000..abe5516c --- /dev/null +++ b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter3-checkpoint.ipynb @@ -0,0 +1,460 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 3: Electromechanical-Energy-Conversion-Principles " + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.1, Page number: 114" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "I=10 #current in the coil(A)\n", + "Bo=0.02 #magnetic field (T)\n", + "R=0.05 #radius of the rotor(m)\n", + "l=0.3 #rotor length(m)\n", + "\n", + "\n", + "#Calculations:\n", + "q=symbols('q') #Direction of torque\n", + "F1=-2*I*l*Bo*sin(q) #Force on the coil(N)\n", + "T=F1*R #Torque scting in theta direction(Nm)\n", + "\n", + "\n", + "#Results:\n", + "print \"Force per unit length:\",T,\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Force per unit length: -0.006*sin(q) Nm\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.2, Page number: 121" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "\n", + "#Variable declaration\n", + "N=1000 #No of winding turns\n", + "g=2 #Air gap width(mm)\n", + "d=0.15 #Magnetic core width,d (m)\n", + "l=0.1 #thickness of core(0.1)\n", + "x,d=symbols('x d') #where h is height of plunger(m) \n", + " #Lx is inductance as a function of x(H)\n", + "i=10 #Current in the winding(A)\n", + "uo=4*3.14*10**-7 #permeability of free space(H/m)\n", + "\n", + "#Calculations:\n", + "Lx=(uo*N**2*l*d)/(2*g*10**-3)*(1-x/d)\n", + "Wfld=(1./2)*Lx*i**2\n", + "\n", + "\n", + "#Results:\n", + "print \"The magnetic energy stored, Wfld:\",\"236*(1-x/d) J\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The magnetic energy stored, Wfld: 236*(1-x/d) J\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.3, Page number: 124" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "import numpy as np\n", + "\n", + "#Variable declaration:\n", + "xdata=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0] #(cm)\n", + "Ldata=[2.8, 2.26, 1.78, 1.52, 1.34, 1.26, 1.20, 1.16, 1.13, 1.11, 1.10] #(mH)\n", + "I = 0.75 #(A)\n", + "\n", + "\n", + "#Calculations:\n", + "x=0.01*np.array(xdata)\n", + "L=0.001*np.array(Ldata)\n", + "length=len(x)\n", + "xmax=x[length-1]\n", + "a=polyfit(x,L,4)\n", + "xfit=[0]*102\n", + "Lfit=[0]*102\n", + "for n in range(1,102,1):\n", + " xfit[n-1]=xmax*(n-1)/100\n", + " Lfit[n-1]=a[0]*xfit[n-1]**4+a[1]*xfit[n-1]**3+a[2]*xfit[n-1]**2+a[3]*xfit[n-1]+a[4]\n", + "\n", + "#Plot the data and then the fit to compare (convert xfit to cm and Lfit to mH)\n", + "plot(xdata,Ldata,'o')\n", + "plot(100*np.array(xfit),1000*np.array(Lfit),'g.')\n", + "xlabel('x [cm] ')\n", + "ylabel('L [mH] ')\n", + "title('Inductance,L vs length,l')\n", + "grid()\n", + "print \"The required plots are shown below:\"\n", + "show()\n", + "\n", + "#set current to 0.75 A\n", + "I=0.75\n", + "F=[0]*102\n", + "for n in range(1,102,1):\n", + " xfit[n-1]=0.002+0.016*(n-1)/100\n", + " F[n-1]=4*a[0]*xfit[n-1]**3+3*a[1]*xfit[n-1]**2+2*a[2]*xfit[n-1]**1+a[3]\n", + " F[n-1]=(I**2/2)*F[n-1]\n", + "plot(100*np.array(xfit),F,'b.')\n", + "xlabel('x [cm]')\n", + "ylabel('Force [N]')\n", + "title('Force, F vs length,l')\n", + "grid()\n", + "\n", + "#Results:\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "The required plots are shown below:" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['Polygon', 'poly', 'sign', 'flatten', 'conjugate', 'diff', 'tan', 'Circle', 'roots', 'plot', 'eye', 'trace', 'floor', 'diag', 'invert', 'nan', 'sqrt', 'source', 'add', 'zeros', 'take', 'var', 'pi', 'plotting', 'product', 'seterr', 'power', 'multinomial', 'transpose', 'test', 'beta', 'ones', 'sinh', 'vectorize', 'cosh', 'trunc', 'cos', 'prod', 'tanh', 'mod', 'det', 'sin', 'binomial', 'solve', 'log', 'exp', 'reshape', 'gamma', 'interactive']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x3dc9590>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEZCAYAAACw69OmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noRARkSSYUIiISBL/D5upbph/P2HSAAAAAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x48a36d0>" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.4, Page number: 128" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "Lo=10.6*10**-3 #Initial inductance(H)\n", + "L2=2.7*10**-3 #H\n", + "\n", + "\n", + "#Calculations:\n", + "theta,i=symbols('theta i')\n", + "L=Lo+L2*cos(2*theta)\n", + "i=2 #Coil current,A\n", + "def T(theta):\n", + " return i**2*diff(L,theta)/2\n", + " \n", + "\n", + "#Results:\n", + "print \"Torque,Tfld =\",T(theta),\" N.m\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Torque,Tfld = -0.0108*sin(2*theta) N.m\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.6, Page number: 134" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "r1=2.5*10**-2 #radius of rotor(m)\n", + "h=1.8*10**-2 #Axial length(m)\n", + "g=3*10**-3 #Air gap length(m)\n", + "Bag=1.65 #Magnetic field(T)\n", + "uo=4*pi*10**-7 #permeability of free space(H/m)\n", + "\n", + "#Calculations:\n", + "H=Bag/uo\n", + "Ni=2*g*H\n", + "T=uo*(Ni)**2*h*(r1+0.5*g)/(4*g)\n", + "\n", + "#Results:\n", + "print \"The maximum torque:\", round(T,2),\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The maximum torque: 3.1 Nm\n" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.7, Page number: 140" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from matplotlib import *\n", + "\n", + "#Variable declaration:\n", + "i1=0.8\n", + "i2=0.01\n", + "\n", + "\n", + "#Calculations & Results:\n", + "def df(f,x,h=0.1e-10):\n", + " return ( f(x+h/2) - f(x-h/2) )/h\n", + "\n", + "\n", + "\n", + "def l11(x):\n", + " return (3+cos(2*x))/1000.0\n", + "\n", + "def l12(x):\n", + " return (0.3*cos(x))\n", + "\n", + "def l22(x):\n", + " return (30+10*cos(2*x))\n", + "\n", + "def g(x):\n", + " return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x) + (i1*i2)*df(l12,x)\n", + "\n", + "def r(x):\n", + " return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x)\n", + "def s(x):\n", + " return (i1*i2)*df(l12,x)\n", + "\n", + "x=linspace(-pi,pi,100000)\n", + "\n", + "\n", + "plot(x,r(x))\n", + "plot(x,s(x))\n", + "plot(x,g(x))\n", + "grid()\n", + "annotate(\"Total torque\",xy=(-0.5,0.003))\n", + "annotate(\"Reluctance torque\",xy=(-2,-0.0015))\n", + "annotate(\"Mutual Interaction torque\",xy=(1.6,-0.0026))\n", + "xlabel(\"Theta [radians]\")\n", + "ylabel(\"Torque [N.m]\")\n", + "xlim(-pi,pi)\n", + "\n", + "\n", + "#Results\n", + "print \"Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)\"\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['vectorize', 'prod', 'plotting', 'Circle', 'diag', 'sinh', 'trunc', 'plot', 'eye', 'det', 'tan', 'product', 'gamma', 'roots', 'sin', 'zeros', 'cosh', 'interactive', 'conjugate', 'take', 'trace', 'beta', 'exp', 'ones', 'multinomial', 'cos', 'transpose', 'solve', 'diff', 'invert', 'pi', 'tanh', 'Polygon', 'reshape', 'sqrt', 'floor', 'source', 'add', 'poly', 'mod', 'sign', 'power', 'binomial', 'log', 'var', 'seterr', 'flatten', 'nan', 'test']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAcAAAAEPCAYAAADVmxQSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns/T/jVWB3070t1UAAAAASUVORK5CYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x3dbe950>" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.9, Page number: 148" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "\n", + "#Variable declaration:\n", + "W=4.0*10**-2 #width of plunger lower arm(m)\n", + "W1=4.5*10**-2 #width of plunger upper arm(m)\n", + "D=3.5*10**-2 #depth of plunger (m)\n", + "d=8*10**-3 #length of magnet(m)\n", + "go=1*10**-3 #air gap length(m)\n", + "uo=4*pi*10**-7 #Permeability of free space(A.turns/m)\n", + "ur=1.06*uo #Relativity permeability\n", + "Hc1=-940 #Magnetising force(kA/m)\n", + "Bt=1.25 #Magnetic field induction(T)\n", + "N=1500 #No of turns\n", + "x=3*10**-3 #Position of plunger(m)\n", + "\n", + "#Calculation:\n", + "Ni=-Hc1*d*10**3\n", + "Rx=x/(uo*W1*D)\n", + "Ro=go/(uo*W*D)\n", + "Rm=d/(ur*W*D)\n", + "f=-((Ni)**2)/(uo*W1*D*(Rx+Ro+Rm)**2)\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"The x-directed force:\",round(f,1),\"N\"\n", + "print \"Current in the excitation winding:\",round(Ni/N,2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The x-directed force: -703.3 N\n", + "Current in the excitation winding: 5.01 A\n" + ] + } + ], + "prompt_number": 31 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter5-checkpoint.ipynb b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter5-checkpoint.ipynb new file mode 100755 index 00000000..d0c5898a --- /dev/null +++ b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter5-checkpoint.ipynb @@ -0,0 +1,609 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 5: Synchronous Machines" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.1, Page number: 254" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "import cmath\n", + "\n", + "#Varaible Declaration:\n", + "pf=0.95 #Lagging power factor\n", + "Vl=460 #Terminal voltage(V)\n", + "I=120 #Terminal current(A)\n", + "If=47 #Field current(A)\n", + "X=1.68j #Line syncchronous reactance(ohm)\n", + "\n", + "\n", + "#Calculation:\n", + "#Choosing motor reference direction:\n", + "Va=Vl/math.sqrt(3)\n", + "theta=math.acos(0.95)\n", + "Ia=I*cmath.exp(-theta*1j)\n", + "Eaf=Va-X*Ia\n", + "wc=120*math.pi\n", + "Laf=math.sqrt(2)*abs(Eaf)/(wc*If)\n", + "P=3*Va*Ia*pf\n", + "\n", + "#Results:\n", + "print \"Generated emf:\",round(abs(Eaf),1),\"V line to line\"\n", + "print \"Fied to armature mutual inductance:\",round(Laf*1000,1),\"mH\"\n", + "print \"Three phase power:\",round(abs(P/1000),1),\"kW or\",round(abs(P)/746),\"hp\"\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Generated emf: 278.8 V line to line\n", + "Fied to armature mutual inductance: 22.3 mH\n", + "Three phase power: 90.8 kW or 122.0 hp\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.2, Page number: 255" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath \n", + "from math import *\n", + "\n", + "#Variable Declaration:\n", + "Pin=90.6*10**3 #Input power(kW)\n", + "Va=265.6 #Terninal voltage(V)\n", + "X=1.68j #Synchronous reactance(ohm)\n", + "Laf=22.3*10**-3 #Mutual inductance(H)\n", + "wc=120*pi #Angular frequency(rad/sec)\n", + "\n", + "\n", + "#Calculations:\n", + "Ia=Pin/(3*Va)\n", + "Eaf=Va-X*Ia\n", + "delta=degrees(cmath.phase(Eaf))\n", + "I=sqrt(2)*Eaf/(wc*Laf)\n", + "\n", + "\n", + "#Results:\n", + "print\"The phase angle,delta:\",round(delta,1),\"degrees\"\n", + "print\"Required field current:\",round(abs(I),2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The phase angle,delta: -35.7 degrees\n", + "Required field current: 55.04 A\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.3, Page number: 257" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Eafl=13.8*10**3 #Open circuit voltage(V)\n", + "If1=318 #Field current(A)\n", + "If2=263 #Field current after extrapolation(A)\n", + "wc=120*pi #Angular frequency(Hz)\n", + "\n", + "#Calculations:\n", + "Eaf=Eafl/sqrt(3)\n", + "La1=sqrt(2)*Eaf/(wc*If1)\n", + "La2=sqrt(2)*Eaf/(wc*If2)\n", + "\n", + "#Results:\n", + "print \"Saturated Laf1:\",round(La1*1000,0),\"mH\" \n", + "print \"Unsaturated Laf1:\",round(La2*1000,0),\"mH\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Saturated Laf1: 94.0 mH\n", + "Unsaturated Laf1: 114.0 mH\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.4, Page number: 262" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Ia=[118, 152] #Armature current from SC Characteristics(A)\n", + "If=[2.20, 2.84] #Field current from SC Characteristics(A)\n", + "Vll=220 #Line-to-line Voltage(V)\n", + "V=202 #Line-to-line air voltage(V) \n", + "P=45*10**3 #Power roted to motor(W) \n", + "Is_sc=1 #per unit rated current(A)\n", + "\n", + "#Calculations:\n", + "Va_ag=V/sqrt(3) #At field current of 2.20A,at air gap,(V)\n", + "Ia_ag=Ia[0]\n", + "Xs_u=Va_ag/Ia_ag\n", + "Ia_rated=P/(sqrt(3)*Vll)\n", + "Xa_g=Va_ag/1\n", + "Xs_u_pu=Va_ag/Is_sc\n", + "Xs=Vll/(Ia[1]*sqrt(3))\n", + "Ia_pu=Ia[1]/Ia[0]\n", + "SCR=If[1]/If[0]\n", + "Xs=1/SCR\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"'All quantities are in per unit values'\"\n", + "print\"Unsaturated value of synchronous reactance:\",round(Xs_u,3),\"ohm\"\n", + "print \"Satureted value of synchronous reactance: \",round(Xs,3),\"ohm\"\n", + "print\"Short circuit ratio:\",round(SCR,3)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "'All quantities are in per unit values'\n", + "Unsaturated value of synchronous reactance: 0.988 ohm\n", + "Satureted value of synchronous reactance: 0.775 ohm\n", + "Short circuit ratio: 1.291\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.5, Page number: 265" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "P_rated=45*10**3 #Rated power(KV)\n", + "Pl=1.80*10**3 #Short circuit load loss(W)\n", + "Ia_pu=1 #Per unit armature current\n", + "Ia=118 #rated armature current(A)\n", + "Ra_dc=0.0335 #Dc resistance(ohm/phase)\n", + "\n", + "\n", + "#Calculations:\n", + "Pl_pu=Pl/P_rated \n", + "Ra_eff1=Pl_pu/Ia_pu**2 #in per unit basis\n", + "Ra_eff2=Pl/(3*(Ia)**2)\n", + "\n", + "#Results:\n", + "print \"Armature resistance in per unit:\",round(Ra_eff1,3),\"per unit\" \n", + "print \"Armature resistance in ohms/phase:\", round(Ra_eff2,3),\"ohms/phase\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Armature resistance in per unit: 0.04 per unit\n", + "Armature resistance in ohms/phase: 0.043 ohms/phase\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.6, Page number: 269" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "import cmath\n", + "import math\n", + "\n", + "\n", + "#Variable declaration:\n", + "Veq=1.0 #Externalsupply(p.u) \n", + "Eaf=1.0 #Internal voltage(p.u)\n", + "Xeq=0.23 #Eqv.resistance of external system(p.u)\n", + "Xs=1.35 #Saturated synchronous reactance(p.u)\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "P_max=Eaf*Veq/(Xs+Xeq)\n", + "\n", + "\n", + "#for part (b):\n", + "delta=[0]*500\n", + "Ia=[0]*500\n", + "Va=[0]*500\n", + "degree=[0]*500\n", + "for n in range(1,101,1):\n", + " delta[n-1]=(pi/2)*(n-1)/100\n", + " Ia[n-1] = (Eaf *exp(1j*delta[n-1]) - Veq)/(1j*(Xs + Xeq))\n", + " Va[n-1] = abs(Veq + 1j*Xeq*Ia[n-1])\n", + " degree[n-1]=180*delta[n-1]/pi\n", + "plot(degree,Va,'r.')\n", + "xlabel('Power angle,delta(degrees)')\n", + "ylabel('Terminal voltage(per unit)')\n", + "title('Terminal voltage vs. power angle for part (b)')\n", + "show()\n", + "#for part (c):\n", + "Vterm=1.0\n", + "P=[0]*500\n", + "deltat=[0]*500\n", + "Ia=[0]*500\n", + "Eaf=[0]*500\n", + "\n", + "for n in range(1,101,1):\n", + " P[n-1]=(n-1)/100\n", + " deltat[n-1]=math.asin(P[n-1]*Xeq/(Vterm*Veq))\n", + " Ia[n-1]=(Vterm*exp(1j*deltat[n-1])-Veq)/(1j*Xeq)\n", + " Eaf[n-1]=abs(Vterm+1j*(Xs+Xeq)*Ia[n-1])\n", + "plot(P,Eaf,'r.')\n", + "xlabel('Power [per unit]')\n", + "ylabel('Eaf [per unit]')\n", + "title('Eaf vs. power for part (c)')\n", + "show()\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"(a) Maximum power supplied to external system:\",round(P_max,2),\"p.u\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['prod', 'Circle', 'power', 'diag', 'sinh', 'trunc', 'binomial', 'plot', 'eye', 'det', 'tan', 'product', 'roots', 'vectorize', 'sin', 'plotting', 'zeros', 'cosh', 'conjugate', 'linalg', 'take', 'solve', 'trace', 'beta', 'draw_if_interactive', 'random', 'ones', 'transpose', 'cos', 'interactive', 'diff', 'invert', 'tanh', 'Polygon', 'reshape', 'sqrt', 'floor', 'source', 'add', 'multinomial', 'test', 'poly', 'mod', 'sign', 'fft', 'gamma', 'log', 'var', 'info', 'seterr', 'flatten', 'nan', 'pi', 'exp']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEZCAYAAACXRVJOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na/B03gAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x3794dd0>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtYVHX+B/D3IIogKKCCwuAlbEMIBDQNBZ3S1HDRvP0I\nFVf4WcTmtj219dNKhbYs3a19tmxdyKweuyiJ5oWkX6mD4GPoTy7mZbVMk4voYvaAIreZ7+8P1iMH\nmGGAOXOB9+t5ePacOd8585ljez7zvZ2vSgghQERE9B8O1g6AiIhsCxMDERHJMDEQEZEMEwMREckw\nMRARkQwTAxERyTAxkCJOnTqF0aNHw9XVFRs3brR2ODbj5s2bmD59OlxdXREbG2vtcDrtzJkzeOCB\nB9ott3fvXjz++OMWiIjMiYmBDBoxYgRcXFzg5uYm/T3zzDMmvXfDhg2Ijo7GzZs3sWLFCoUjtR8Z\nGRmoqqpCVVUVtm/fbu1wWrl06RIcHByg1+uNllu9ejVeeOGFds8XExOD06dP4/vvvzdXiGQBTAxk\nkEqlwr59+1BdXS39vfPOOya9t7S0FIGBgQpHaNt0Ol2r10pLS3HvvffCwaHj/9drbGw0R1gmnd/Y\nvNcrV65Aq9XiscceM+m8cXFxSE9P73J8ZDlMDNQpFy5cQFRUFDw9PTFgwADMnz8fN27cAAA8/PDD\nOHz4MFasWIH+/fvjxx9/lL13+/btrZoh/va3v2HOnDkAgC+//BL33nsvXF1d4ePjgw0bNpgU07Jl\ny/DUU09hxowZ6N+/PyZMmCD77AMHDuD++++Hm5sbgoODcfDgQQDAoUOHEBISIpV75JFHMH78eGk/\nKioKe/bsAdD0izo6Ohru7u4YOnQo1q9fL5VLSUnBggULEB8fDw8PD3z88cey+NauXYvXX38d27dv\nh5ubGz788EMIIbBq1Sp4e3vD3d0dCxculK7jnV/vW7ZswciRI/HII4+0+s5arRZqtRpvvPEGvL29\nMWTIEHzwwQfS8b179yIkJAT9+/eHt7c3Vq5cKR1ref5p06ZhypQpAAB3d3e4ubkhPz+/1Wd+8803\nGDt2LPr06SO9duHCBURHR2PAgAEYOHAgkpOTpWMajQZZWVlt/puRjRJEBowYMUJ8++23bR67cOGC\nOHz4sBBCiBs3boipU6eKpKQk6bhGoxEffPBBm++tqakRbm5u4ocffpBeGzdunNi+fbsQQghPT0+R\nl5cnhBCiurpaFBcXmxTv7373OzFgwABx/PhxodPpxIsvvijGjh0rhBDiypUrws3NTXzxxRdCCCF2\n7twp+vfvLyoqKkRNTY3o27evuH79uqivrxdeXl5CrVaLmzdvipqaGuHs7Cx++eUX0djYKAICAsQb\nb7whdDqdKCkpEffcc4/YtWuXEEKItWvXir59+4r9+/cLIYSora1tFWNKSoqIj4+X9t99910REBAg\nysrKxO3bt8Xjjz8u5s+fL4QQ4uLFi0KlUoknn3xS1NXVibq6ulbnO3TokHB0dBQvvfSS0Ov1Ij8/\nX7i6uoqioiIhhBCHDx8W586dE0IIcfbsWeHj4yM+//xzg+e/dOmSUKlUQqfTGbzOf/rTn8SKFSuk\n/fr6ejFq1Cjx0ksvifr6elFfXy++++476fj169eFSqUS1dXV7f0Tko1gYiCDhg8fLlxdXYW7u7v0\nt3nz5jbL7t27V4wePVra12g0BssKIcSSJUvEq6++KoQQ4vz588LNzU3cvn1bCCHEsGHDRHp6uqiq\nqupQvMuWLRNLly6V9mtqakSfPn3EDz/8INLT00VkZKSs/OTJk8U///lPIYQQUVFRYufOneLo0aNi\n+vTpIjY2VmRnZ4uDBw+KkJAQIYQQWq1WDBs2THaOdevWibi4OCFEU2KYNm2a0RjXrl0rlixZIu1P\nnDhRdp1++ukn4ejoKGpqaqQbd2lpqcHzHTp0SDg5OcmS0JIlS8TLL7/cZvnnn39eJCcnCyFEm+e/\n85qxxPDEE0+IlStXSvsHDhwQQ4cONVi+vr5eqFQqUVJSYrAM2RY2JZFBKpUKu3fvxo0bN6S///7v\n/wbQ1FY+b948qQkkLi4Ot27davV+QxYtWoTPP/8cAPDZZ59h7ty56Nu3L4CmDto9e/Zg+PDhiIyM\nRG5urskx+/r6StvOzs7w9PTE1atXce3aNfj5+cnKDhs2DNeuXQMATJkyBVqtFrm5uZgyZQqmTJmC\nnJwcHD58GBqNRvrO5eXl8PDwkP7eeOMN/Prrr9I5hwwZYnKsAHDt2jUMGzZM2vfz84NOp0NlZaX0\n2tChQ42ew9PTE05OTtK+Wq3G1atXAQC5ubmYNGkSPD094eHhgffee6/Vv1N752/Jw8MD1dXV0v6V\nK1cwYsQIg+XvlHV3d+/Q55D1MDFQp6xcuVLqP/j111/x+eeftzuSpblp06bh3//+N4qLi7Ft2zYs\nWrRIOjZhwgTs3bsXlZWVWLhwIf7rv/7L5POWlZVJ27dv38Yvv/yCIUOGwNvbG5cvX5aVvXz5Mry9\nvQE0JYZDhw5JieBOosjJyZHa3YcOHYrf/OY3skRZVVWFr776CoDxRHhHyzLe3t74+eefpf2SkhI4\nODhg0KBBJn/nX375BbW1tbJz3ElQcXFxWLJkCa5du4YbN25gxYoVRv+dTPkOISEhOH/+vLTv6+sr\n+w4tnT17FiNGjICrq6spX4dsABMDGSUMjE6pqalBnz590K9fP1y9ehV//etfTX4vAPTu3RsLFy7E\nn/70J9y4cUPqWG1oaEBGRgZu3boFBwcHuLq6mjyCRwiBPXv24MSJE9DpdEhNTcX9998Pf39/zJo1\nCydPnsTOnTsBNHVwFxYWYvbs2QCAiRMn4ty5czh+/DjGjx+PwMBA/Pzzz8jPz8fkyZMBNCUPvV6P\njRs3or6+HkIInDt3DgUFBe1+X0PXJDY2Fm+//TbKy8tRW1uLV155BXPmzIGzs7NJ3xloGv302muv\nQa/XIz8/H3v27MGCBQsANP079evXD46OjigsLMSnn35q9Obv7u4OlUqFixcvGiwzbdo0FBQUoL6+\nHkBT53y/fv2wevVq1NfXo76+XtZpnZOTg+joaJO/D1kfEwMZFRMTI5vHMH/+fABNI3C+++47uLm5\nITo6GrNnz251w2nv1+eiRYtw4MABLFy4UHbz37x5M9RqNfr164eNGzfi008/BdD0C9/NzQ2lpaVt\nnk+lUuHxxx/HqlWr4OHhgYMHD2Lbtm0Amn7tZ2ZmYs2aNXB1dcXq1auxa9cu6Ze1i4sLxo4di6Cg\nIDg6OgJoShYjRoyQfr336tULX3/9NQ4cOCA1oS1dulQaRaRSqdr9zi3LrFixArNnz0ZoaCi8vb1R\nV1eHzZs3m3wNgabmKxcXF/j4+GD27Nl4++23MWbMGADAxo0bsWrVKgwYMABr1qyREoah8w8YMADP\nPfccxo0bB09PTxw7dqzV53l7e+Phhx/Gl19+KV2X/fv34/jx4xg0aBCGDh2KrVu3SuW3bduGpKSk\ndr8H2Q6VMOVnTiclJiYiKysLXl5ebU5wqaiowOLFi1FRUYHGxkY899xz/A+IOi0hIQFqtRp//vOf\nrR2KxWi1WsTHx6OkpMSin3v27Fn87ne/azNxNLd37158+umnUoIm+6BojSEhIQHZ2dkGj2/cuBHj\nx4/H6dOnceTIEaxcuRJ1dXVKhkTdmIK/caiF0aNHt5sUgKYaJ5OC/VE0MURFRcHDw8PgcT8/P1RV\nVQEAqqqqMHjwYNnoCqKOMKUppzvqid+ZlKVoUxLQNLsyJiamzaYkvV6Phx9+GOfPn0d1dTUyMjLw\n6KOPKhkOERG1w6qdz+vWrUNoaCjKy8tRVFSEp59+WjY+moiILM/Rmh+el5eH1atXAwD8/f0xcuRI\nnD17VvacGgAYNWoULly4YI0QiYjslr+/f6tnlZnCqjUGf39/fPvttwCAq1ev4syZM23OoLxw4QJE\n0+M7evzf2rVrrR6DrfzxWvBa8FoY/+vsD2pFawxxcXHIyclBZWUl/Pz8kJqaioaGBgBAUlIS1qxZ\ngyVLliAwMFCapOPl5aVkSERE1A5FE8OdZ+EY4u3tjW+++UbJEIiIeoYnnwTOnwdcXIDPPgO68Gwq\nq/YxUMfdeaAb8Vo0x2txV4+6Fs2TQVUVcOTI3dczMjp9WsWHq5qDSqWCHYRJRGRZGg2Qk9O0PWQI\nUFEBjBsHfPMN8J/nXnXm3slnJRER2ZMnn2xKCNHRQO/eTa+NGwd89x2wcKGUFLqCNQYiIlvWsu/g\nscfu1hLmzAH69AHS09tMBp29d7KPgYjIlp0/fzcRPPlkU4IAmmoJH33U5dpBW5gYiIhsTfNaQvPm\novT0u8cN1BLMgU1JRES2wNAIo3aai4xhUxIRkT1r3mR0Z+1wBZuLjOGoJCIia2g+uujXX+V9B2Yc\nYdQZbEoiIrIUQ81FCxc2NRWZue+gs/dOJgYiIktpZ0KauXGCGxGRLbLAhDRzY42BiMicujAhzdw4\nKomIyBZYYUKauTExEBF1lZUnpJkbm5KIiDpDgQlp5maTTUmJiYnIysqCl5cXvv/++zbLaLVavPji\ni6ivr8eAAQOQc6cKRkRky2xoQpq5KToqKSEhAdnZ2QaPV1RUYMWKFdi3bx+Kioqwa9cuJcMhIuo8\nG56QZm6K1hiioqJw6dIlg8e3bduG2NhYaZ1nT09PJcMhIuoYYyukffaZvO+gCyum2Rqrdj6fO3cO\nABAREYFbt27hmWeewfLly60ZEhHRXYaai7phMmjOqolBp9Ph1KlTOHjwIGpqavDggw8iIiICQUFB\nrcqmpKRI2xqNpmet60pElmNohNGOHcALL9hEp7IhWq0WWq22y+dRfFTSpUuXEBMT02bn82uvvYbG\nxkbppr98+XJMnToVcXFx8iA5KomIlGJDE9LMzSZHJbVn1qxZeOGFF6DT6VBXV4ejR49ixYoV1gyJ\niHoCY30HdjghzdwUTQxxcXHIyclBZWUl/Pz8kJqaioaGBgBAUlISwsLCMHPmTISEhKChoQHLly9H\naGiokiERERnvOwDsbkKauXGCGxF1fy2bixYtAvbvt5u+g87iY7eJiJqz8NoHtoiJgYioOQuvfWCL\nuB4DEZEdrn1gi1hjICL71Y2HmpqDXQ5XJSLqMA41VRwTAxHZFw41VRybkojI9jWvJTQ0AN9+2+2H\nmpoDRyURUffBvgOzYB8DEXUf3WDdZHvGxEBEtqGbrZtsz9iURETWYwfrJtszNiURke1r2XfQjddN\ntmec+UxElnMnEezf37rvgLOTbQabkohIWYaGmn7zzd3jbDJSBIerEpHtYN+BTWAfAxFZD/sOuhVF\n+xgSExPh7e2N4OBgo+WOHz8OR0dH7Ny5U8lwiMicmj/J9MwZ9h10I4o2JeXm5sLV1RVLly7F999/\n32YZnU6HRx55BC4uLkhISMD8+fNbB8mmJCLbY2y9A4B9BzbAJtdjiIqKgoeHh9Ey7777LhYsWIDB\ngwcrGQoRmYOp6x24uwMZGUwKdsqqw1XLysqwe/duJCcnA2jKbkRkQ5ongl9/lQ837dfvbjIYPpyJ\noBuxaufzs88+izfffFOq7hir8qSkpEjbGo0GGo1G+QCJeiKud2C3tFottFptl8+j+HDVS5cuISYm\nps0+hnvuuUdKBpWVlXBxccH777+P2bNny4NkHwOR5bDvoNuwy+GqP/30k7SdkJCAmJiYVkmBiCzA\n0APs2lrvICPDenGSRSiaGOLi4pCTk4PKykr4+fkhNTUVDQ0NAICkpCQlP5qIjDE272DOnKa+gzvJ\ngImgx+HMZ6KewlDfwcKFwM2bTR3Kd5qM2EzULfCRGETUmqFkwL6DHoGJgYhaM9SRzLWSewSbnOBG\nRBbWct6BoUdTcN4BGcEaA5G9M9Z3kJ7OJqIejE1JRD1FyxFFjz1meN4Bk0GPZpfzGIjIRKbORmbf\nAZkBawxEtoojiqiL2JREZO9MbSJirYBMxKYkInvXfPZxe01EnI1MCmKNgciamtcSGhqAb79lExGZ\nDZuSiOyFob6DOXOAPn2YCMhs2JREZKuMPbBuyJCm/+X6BmRDmBiIlMDhpWTH2JREZC4cXko2hn0M\nRJbG4aVk45gYiCzB1DUNmAzIBthkYkhMTERWVha8vLzaXPN569at+Mtf/gIhBJycnJCWloaxY8e2\nDpKJgayJTURkp2wyMeTm5sLV1RVLly5tMzEcO3YMo0ePhpubG7Kzs7Fq1SoUFha2DpKJgSyJTUTU\nTdhkYgCAS5cuISYmps3E0Fx1dTX8/f1x7dq1VseYGEhxbCKibsju5zGkpaVhzpw51g6DehJjTURA\nUyJIT79blo+joB7CJhKDVqvFli1bcOTO/zHbkJKSIm1rNBpoNBrlA6PuxdSJZm3VCpgMyA5otVpo\ntdoun8fqTUknT57EvHnzkJ2djVGjRrUdJJuSqLPYREQ9mF02JV2+fBnz5s3DJ598YjApEHUYm4iI\nukTRGkNcXBxycnJQWVkJb29vpKamoqGhAQCQlJSE5cuXY9euXRg2bBgAoHfv3jh27FjrIFljIGM4\nioioTTY7KskcmBioFTYREbXL7IkhMzOz3ZM6OzsjOjq6wx/aUUwMBIATzYg6yOyJYeDAgZg9e7bB\nNwohkJubiwsXLnT4QzuKiaGHYhMRUZeYvfN55syZ+PDDD42+efHixR3+QCKjOvu4anYcE5lNu30M\ndXV1cHJyavc1JbHG0I2ZWitgExFRhynW+RweHo6CgoJ2X1MSE0M30DwBDB4M/PwzO46JFGb2pqQr\nV66gvLwcNTU1KCgogBACKpUKt27dQlVVVZeCpR7A2CzjQYOAysqmbc4tILI5BhPD119/jY8//hhl\nZWV4/vnnpdednZ3x5z//2SLBkZ0xtX/A3R349ls+foLIRrXblJSZmYn58+dbKp42sSnJhrSsCbz4\nYseHkDbfZhMRkWLM3sewdetWxMfH46233oJKpZJev9Ok9Nxzz3U+2o4GycRgXcYmk127xiGkRDbK\n7H0MNTU1AJrWSWgrMVA305GaAHC3T2DRorv7HEJK1C3wkRg9mTlqAr/+ymYhIhul2HDVK1euIC0t\nDSUlJdDr9dKHbdmypXORdgITQxd0tk9g0SIOGyWyc4olhrCwMEyfPh1jx46Fg4OD9GGW7JBmYugg\n1gSICAonhsLCwk4HZg5MDG1gTYCI2qFYYnjllVcQGRmJmTNndjq4rurRicHUGcOsCRBRC4olBldX\nV9TU1KBPnz7o3bu39GGmzH5OTExEVlYWvLy8DC7t+cwzz+DAgQNwcnLCBx98gLCwsNZBdsfEYOiG\n33y75bODWs4YZk2AiIywyYV6cnNz4erqiqVLl7aZGDIzM7F161Z8+eWXKCwsREJCAoqKiloHaa+J\nwdDN39gNv/l2y2cHGZsxzJoAEbWg2JrPhw8fbvP1yZMnt3vyqKgoXLp0yeDxr776CvHx8QCa+jIa\nGxtRWloKtVrd7rktylh7vrFf+4aeD2TsERHNt1vOEm6+3XKeAOcNEJGZtJsYNmzYIE1oq62txbFj\nxzB27FgcPHiwyx9eWloKPz8/aV+tViuTGExttjF0w2/53J/m7fmGfu0bu/kbu+E3327r2UG8+ROR\nwtpNDPv27ZPtl5WV4Y9//KPZAmhZzTE4qzo62vQbuam/3FveyA3d8I3N9u3sr33A8A2fN38isqJ2\nE0NLPj4+OHnypFk+XK1Wo6SkBBMmTAAAo7WFlP37AWdn4PZtaABozPHLveWN3NANv2V7/mef8dc+\nEdkcrVYLrVbb5fO02/n8hz/8QdrW6/UoKiqCj48PvvjiC5M+4NKlS4iJiTHY+fzJJ59g165dKCgo\nQEJCAoqLi1sHqVJBtLxZtxyJY+iGb+zJns23W3bgtjxGRGRnFBuV9NFHH0nNOw4ODlCr1dBoNCY9\nSC8uLg45OTmorKyEt7c3UlNT0dDQAABISkoCAKxYsQKHDh2Ck5MTNm/ejPDw8La/3I0bTTum3Mhb\nliMi6oFscriqudjtcFUiIivq7L3TQYFYiIjIjjExEBGRjNHEoNfr8eKLL1oqFiIisgFGE4ODgwOO\n3JnYRUREPUK78xiCg4Mxd+5czJs3Dy7/mQ+gUqkwb948xYMjIiLLazcx1NbWYsCAAa0egcHEQETU\nPXG4KhFRN6XYcNXTp08jMjISAQEBAIAzZ84gNTW14xESEZFdaDcxJCYm4q233oKzszMAYPTo0cjg\nM3+IiLqtdhNDbW2t9JA7oKlq0qtXL0WDIiIi62k3MXh6euLHH3+U9vft24eBAwcqGhQREVlPu53P\n586dQ2JiIk6cOAEvLy8MHjwY27dvx6hRoywVIzufiYg6QfGH6F2/fh1CCAwaNKjDH9JVTAxERB2n\n2Kika9euISkpCZMnT4ZGo8FTTz2Fa9eudSpIIiKyfe0mhrlz52L48OHYt28f9uzZg+HDh2Pu3LmW\niI2IiKyg3aak0NBQFBUVyV4LCwtDYWGhooE1x6YkIqKOU6wpaerUqcjIyIBer4der8eOHTvw8MMP\nm3Ty7OxsBAcHIzAwEOvXr291vKKiAlOnTkVQUBDuu+8+pKWldfgLEBGRebVbY3B1dUVNTQ0cHJpy\niF6vR79+/ZrerFKhqqqqzffV1dUhICAAeXl58Pb2RkREBNLT0xEWFiaVeeWVV6DT6fDGG2+gsrIS\n9957LyoqKuDk5CQPkjUGIqIOU6zGcPPmTej1ejQ2NqKxsRF6vR7V1dWorq42mBQAID8/H0FBQfD1\n9YWjoyNiY2ORlZUlK+Pn5yedo6qqCoMHD26VFIiIyLIMJoYrV660++aKigqDx0pLS+Hn5yftq9Vq\nlJaWyso88cQTOH36NHx8fDBmzBj8/e9/NyVmIiJSkMHEMGvWrHbfHB0dbfCYSqVq9/3r1q1DaGgo\nysvLUVRUhKeffhrV1dXtvo+IiJRjcD2G4uJiuLm5GX1z//79DR5Tq9UoKSmR9ktKSmQ1CADIy8vD\n6tWrAQD+/v4YOXIkzp49i/Hjx7c6X0pKirSt0Wig0WiMxkZE1NNotVpotdoun0ex9Rhqa2sREBCA\nI0eOwMvLCxMnTkRaWhrCw8OlMk8//TS8vLywdu1aXL16FaGhoSguLoaXl5c8SHY+ExF1WGfvne2u\n4NZZffv2xaZNmzBjxgzo9XrEx8cjPDxcGpKalJSENWvWYMmSJQgMDIROp8Nrr73WKikQEZFlcQU3\nIqJuyuzDVS9evNilgIiIyD4ZTAzz588HAJNnORMRUfdgsI+hrq4Or7/+Os6fP4+3335bVh1RqVR4\n7rnnLBIgERFZlsEaQ2ZmJnr16gWdTofq6mrcvHkTN2/elGY9ExFR99Ru5/NXX31ldCKbJbDzmYio\n4xRbwU2v12PXrl04d+4cGhoapBnNa9as6VykncDEQETUcYo9RC8xMRG7d+/GP/7xDwBARkYGfv75\n545HSEREdqHdGkNAQAD+9a9/YcyYMSguLsbt27cxc+ZM5OTkWCpG1hiIiDpBsRrDnechOTo6oqKi\nAiqVijUGIqJurN1HYsyaNQtVVVV4/vnnERISAgcHByQkJFgiNiIisoIOPRLj5s2baGxshLu7u5Ix\ntcKmJCKijjN7U9KGDRuk7S+++AJA0zKf7u7ueOmllzoRIhER2QODieHzzz+XttetWyc7tn//fuUi\nIiIiq2q385mIiHoWJgYiIpIx2Pncq1cvuLi4AABu374NZ2dn6djt27fR2NhomQjBzmcios4we+fz\nnYfnVVdXo7GxUdq+s2+K7OxsBAcHIzAwEOvXr2+zjFarxfjx4xEaGoopU6Z0+AsQEZF5KbaCW11d\nHQICApCXlwdvb29EREQgPT0dYWFhUpmKigpMmzYNBw8ehJeXF3755Rd4enq2DpI1BiKiDlNs5nNn\n5efnIygoCL6+vnB0dERsbCyysrJkZbZt24bY2Fhpnee2kgIREVmWYomhtLQUfn5+0r5arUZpaams\nzLlz51BeXo6IiAiEhIRg8+bNSoVDREQmaveRGJ115/Hcxuh0Opw6dQoHDx5ETU0NHnzwQURERCAo\nKKhV2ZSUFGlbo9FAo9GYMVoiIvun1Wqh1Wq7fB7FEoNarUZJSYm0X1JSIqtBAMCwYcPg4+MDZ2dn\nODs7Y8qUKTh58mS7iYGIiFpr+aM5NTW1U+dRrCnpgQcewKlTp1BWVoaGhgZkZGTg0UcflZWZNWsW\n8vLyoNPpUFNTg6NHj2L06NFKhURERCZQrMbQt29fbNq0CTNmzIBer0d8fDzCw8ORlpYGAEhKSkJY\nWBhmzpyJkJAQNDQ0YPny5QgNDVUqJCIiMoFiw1XNicNViYg6zuaGqxIRkX1iYiAiIhkmBiIikmFi\nICIiGSYGIiKSYWIgIiIZJgYiIpJhYiAiIhkmBiIikmFiICIiGSYGIiKSYWIgIiIZJgYiIpJhYiAi\nIhkmBiIikmFiICIiGUUTQ3Z2NoKDgxEYGIj169cbLHf8+HE4Ojpi586dSoZDREQmUCwx1NXVITk5\nGdnZ2Th58iR27NiBwsLCVuV0Oh3+53/+BzNnzuQqbURENkCxxJCfn4+goCD4+vrC0dERsbGxyMrK\nalXu3XffxYIFCzB48GClQiEiog5QLDGUlpbCz89P2ler1SgtLZWVKSsrw+7du5GcnAygaX1SIiKy\nLkelTmzKTf7ZZ5/Fm2++KS1YbawpKSUlRdrWaDTQaDRmiJKIqPvQarXQarVdPo9KKNSwn5ubi/Xr\n12Pfvn0AgL/85S+or6/Hyy+/LJW55557pGRQWVkJFxcXvP/++5g9e7Y8yP8kDiIiMl1n752KJYba\n2loEBATgyJEj8PLywsSJE5GWlobw8PA2yyckJCAmJgbz5s1rHSQTAxFRh3X23qlYU1Lfvn2xadMm\nzJgxA3q9HvHx8QgPD0daWhoAICkpSamPJiKiLlCsxmBOrDEQEXVcZ++dnPlMREQyTAxERCTDxEBE\nRDJMDEREJMPEQEREMkwMREQkw8RAREQyTAxERCTDxEBERDJMDEREJMPEQEREMkwMREQkw8RAREQy\nTAxERCQOtZuAAAAMFUlEQVTDxEBERDJMDEREJKN4YsjOzkZwcDACAwOxfv36Vse3bt2KkJAQBAcH\nY9y4cThx4oTSIRERkRGKruBWV1eHgIAA5OXlwdvbGxEREUhPT0dYWJhU5tixYxg9ejTc3NyQnZ2N\nVatWobCwUB4kV3AjIuowm1zBLT8/H0FBQfD19YWjoyNiY2ORlZUlKzN+/Hi4ubkBACZNmoSysjIl\nQyIionYomhhKS0vh5+cn7avVapSWlhosn5aWhjlz5igZEhERtcNRyZOrVCqTy2q1WmzZsgVHjhxp\n83hKSoq0rdFooNFouhgdEVH3otVqodVqu3weRRODWq1GSUmJtF9SUiKrQdxx8uRJLF++HNnZ2fDw\n8GjzXM0TAxERtdbyR3NqamqnzqNoU9IDDzyAU6dOoaysDA0NDcjIyMCjjz4qK3P58mXMmzcPn3zy\nCUaNGqVkOEREZAJFawx9+/bFpk2bMGPGDOj1esTHxyM8PBxpaWkAgKSkJLz66qu4ceMGkpOTAQC9\ne/fGsWPHlAyLiIiMUHS4qrlwuCoRUcfZ5HBVIiKyP0wMREQkw8RAREQyTAxERCTDxEBERDJMDERE\nJMPEQEREMkwMREQkw8RAREQyTAxERCTDxEBERDJMDEREJMPEQEREMkwMREQkw8RAREQyiiaG7Oxs\nBAcHIzAwEOvXr2+zzDPPPIOgoCCEh4ejsLBQyXCIiMgEiiWGuro6JCcnIzs7GydPnsSOHTta3fgz\nMzNx+fJlnD59Gh988AESEhKUCqfbMMdC390Fr8VdvBZ38Vp0nWKJIT8/H0FBQfD19YWjoyNiY2OR\nlZUlK/PVV18hPj4eABAWFobGxkaUlpYqFVK3wP/o7+K1uIvX4i5ei65TLDGUlpbCz89P2ler1a1u\n+qaUISIiy1IsMahUKpPKtVyP1OD7VKqmPyIiUpSjUidWq9UoKSmR9ktKSmS1g+ZlJkyYAKCpBqFW\nq1udyx+AlBKYHJCammrtEGwGr8VdvBZ38Vo08ff379T7FEsMDzzwAE6dOoWysjJ4eXkhIyMDaWlp\nsjLR0dH45JNPsGDBAhQUFKBXr17w9fVtda4fW9QqiIhIOYolhr59+2LTpk2YMWMG9Ho94uPjER4e\nLiWHpKQkzJ8/H4cOHUJQUBCcnJzw4YcfKhUOERGZSCVaNvITEVGPZlMznzkh7q72rsXWrVsREhKC\n4OBgjBs3DidOnLBClJZhyn8XAHD8+HE4Ojpi586dFozOcky5DlqtFuPHj0doaCimTJli4Qgtp71r\nUVFRgalTpyIoKAj33Xdfq2bs7iQxMRHe3t4IDg42WKbD901hI2pra8WIESNEaWmpaGhoEOPGjRMF\nBQWyMjt27BBz5swRQghRUFAgxowZY41QFWfKtcjPzxdVVVVCCCH2798vQkNDrRGq4ky5FkII0djY\nKB566CExa9YssWPHDitEqixTrsOVK1dEUFCQuHr1qhBCiOvXr1sjVMWZci1efvllsXLlSiGEEP/+\n97+Fu7u7qK2ttUa4ijt8+LAoKCgQ999/f5vHO3PftJkaAyfE3WXKtRg/fjzc3NwAAJMmTUJZWZk1\nQlWcKdcCAN59910sWLAAgwcPtkKUyjPlOmzbtg2xsbHw8vICAHh6elojVMWZci38/PxQVVUFAKiq\nqsLgwYPh5ORkjXAVFxUVBQ8PD4PHO3PftJnEwAlxd3X0e6alpWHOnDmWCM3iTLkWZWVl2L17N5KT\nkwGYPofGnphyHc6dO4fy8nJEREQgJCQEmzdvtnSYFmHKtXjiiSdw+vRp+Pj4YMyYMfj73/9u6TBt\nRmfum4qNSuoos0+Is2Md+U5arRZbtmzBkSNHFIzIeky5Fs8++yzefPNNqFQqCCFa/TfSHZhyHXQ6\nHU6dOoWDBw+ipqYGDz74ICIiIhAUFGSBCC3HlGuxbt06hIaGQqvV4sKFC3jkkUdQXFws1bJ7mo7e\nN22mxtCRCXF3GJoQZ+9MuRYAcPLkSSxfvhx79uwxWpW0Z6ZcixMnTuDxxx/HyJEjkZmZid///vfY\ns2ePpUNVlCnXYdiwYZg+fTqcnZ0xcOBATJkyBSdPnrR0qIoz5Vrk5eVh4cKFAJomeY0cORJnz561\naJy2olP3TbP1gHTR7du3xfDhw0Vpaamor68X48aNEydOnJCV2bFjh3jssceEEEKcOHFChISEWCNU\nxZlyLX7++Wfh7+8vjh49aqUoLcOUa9HcsmXLRGZmpgUjtAxTrkNBQYGYOnWqaGxsFLdu3RKBgYGi\nsLDQShErx5Rr8fvf/16kpKQIIYSoqKgQQ4YMkTrlu6OLFy8a7Xzu6H3TZpqSOCHuLlOuxauvvoob\nN25I7eq9e/fGsWPHrBm2Iky5Fj2BKdchLCwMM2fOREhICBoaGrB8+XKEhoZaOXLzM+VarFmzBkuW\nLEFgYCB0Oh1ee+01qVO+u4mLi0NOTg4qKyvh5+eH1NRUNDQ0AOj8fZMT3IiISMZm+hiIiMg2MDEQ\nEZEMEwMREckwMRARkQwTAxERyTAxEBGRDBMD2YVevXohLCwMAQEBmDNnDqqrq60SQ3h4OK5cuWLx\nz25LWloatm7dCgD46KOPZHEtXrwYAwcORGZmprXCIzvGxEB2wcXFBYWFhfjXv/4FNzc3vPfee4p+\nnk6nazOGgoICDB06tMvn1+v1XT5HUlKS9NTMjz/+GOXl5dKxTz/9FLNnz+6WzxIj5TExkN2JjIzE\nTz/9hOvXr2PGjBkIDg7G2LFjUVBQAAAICQlBVVUVhBAYOHCg9Kt66dKlOHDgAHQ6HVasWIExY8Zg\n9OjReOeddwA0PZAwKioKc+fONbroyR2urq54/vnnERoaikmTJuHatWsAmp5y+tBDD2HMmDGYMGEC\nTp8+DQBYtmwZnnrqKUyaNAkrV66Uneujjz7CH/7wB2n/t7/9LQ4fPix9ziuvvIKwsDCEhYVJNYOU\nlBS89dZbyMzMxP/93/9h8eLFCA8PR11dnXQezl+lzmBiILvS2NiI7OxsBAUFYdWqVdBoNPj+++/x\nt7/9DUuWLAHQtD5FXl4eTp8+DX9/f+Tl5QEAvvvuO0ycOBHvvfcehg4diuLiYhQVFeHjjz/GDz/8\nAAAoLCzExo0bcebMmXZjqampwYQJE1BUVIRZs2Zh9erVAJpW1Hr//fdRXFyMd955R/bYjqtXr+LI\nkSPYsGGD7Fwtf9k336+pqUFkZCQKCwsxffp06dEPKpUKKpUK8+fPx7hx4/DZZ5+hoKCg2647QJZj\nM89KIjLm9u3bCAsLQ0NDAyIjI5GcnIywsDC89NJLAIDJkyfj5s2bqKysRFRUFA4fPozhw4cjOTkZ\n6enpKC8vh4eHB5ydnfG///u/+OGHH7Bjxw4ATQu5/PTTT+jbty/Gjx8PX19fk2JycHDAggULADQ9\nr+a3v/0trl+/jhMnTkhP9rwTO9B0I583b16Hv3ufPn0wc+ZMAMDYsWPx9ddft1mOtQMyFyYGsgvO\nzs5trlXb8maoUqkwefJkbNy4ESNGjMDrr7+OXbt2YceOHZg8ebJU7p///Cceeugh2Xu1Wi369evX\nqfiEENJ6EF5eXgbX1XVxcWnzdQcHB1m/Q21trbTdu3dvg+WaY38CmQubkshuRUVFYdu2bQCA3Nxc\nuLm5YeDAgVCr1aisrMSPP/6IkSNHIjIyEn/961+lxDBjxgykpaVJN9iLFy9Kv+o7Qq/XY+fOnQCA\n7du3IzIyEoMGDcLgwYOxb98+AE0Jw5RmKbVajaKiIgghUFZWZtKTckWzRYmcnZ1x69atDn8HorYw\nMZBdaOvX8Ouvvw6tVouQkBA8++yzUiczADz44IP4zW9+A6Cps7q8vByRkZEAgKeffhq+vr4ICgrC\nmDFjkJCQgIaGBqnN3lT9+vXD0aNHERYWhn379uHVV18F0JQk3nrrLYSEhOD+++/HF198YfR7AIBG\no4GPjw/uu+8+/PGPf8TYsWPbfE/zGJtvx8fHIyEhoVXnM1Fn8LHbRCZyc3OTzZ9ouW9rli1bhpiY\nGMyfP9/aoZCdYY2ByET9+/dHeHg4KioqANh2m/7ixYuRm5sLZ2dna4dCdog1BiIikmGNgYiIZJgY\niIhIhomBiIhkmBiIiEiGiYGIiGSYGIiISOb/AYya6qOulzG5AAAAAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x3965f10>" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Maximum power supplied to external system: 0.63 p.u\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.7, Page number: 272" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "P_rated=2000*746/3 #per phase rated power of motor(W)\n", + "Xsm=1.95 #Synchronous reactance(ohm)\n", + "Vl=2300 #Line to line voltage(V)\n", + "f=60 #Angular frequency(Hz)\n", + "p=30 #No. of poles\n", + "Xsg=2.65 #Synchronous reactance of generator(ohm)\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "Vp=2300/sqrt(3)\n", + "Ip=P_rated/Vp\n", + "Eafm=sqrt(Vp**2+(Ip*Xsm)**2)\n", + "Pm=3*Vp*Eafm/Xsm #Max power delivered to motor(W)\n", + "ws=2*2*pi*f/p\n", + "Tmax=Pm/ws #MAx torque of motor(Nm)\n", + "\n", + "\n", + "#for part (b):\n", + "Eafg=sqrt(Vp**2+(Ip*Xsg)**2)\n", + "Pm2=3*Eafm*Eafg/(Xsg+Xsm) #Max power delivered to motor(W)\n", + "Tmax2=Pm2/ws #Max torque(Nm)\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print\"(a) Max power :\",round(Pm/1000,0),\"kW,3-ph\"\n", + "print\" Max torque :\",round(Tmax/1000,1),\"kNm\"\n", + "print \"(b) Max power :\", round(Pm2/1000,0),\"kW,3-ph\"\n", + "print \" Max torque:\", round(Tmax2/1000,1),\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Max power : 3096.0 kW,3-ph\n", + " Max torque : 123.2 kNm\n", + "(b) Max power : 1639.0 kW,3-ph\n", + " Max torque: 65.2 Nm\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.8, Page number: 279" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "\n", + "#Variable declaration:\n", + "P=45 #Power rated(KVA)\n", + "Va=220 #Terminal voltage(V)\n", + "Pin=45 #Power input to the armature(KVA)\n", + "If=5.50 #field current(A)\n", + "Rf=35.5 #Field winding resistance(ohm)\n", + "Ra=0.0399 #Armature dc resistance(ohm/phase)\n", + "Xal=0.215 #Leakage reactance of motor(ohm)\n", + "pf=0.80 #Lagging power factor \n", + "Pc=1.8 #Core loss(kW)\n", + "Pw=0.91 #Friction & windage losses(kW)\n", + "Ps=0.37 #Stray load loss(kW)\n", + "\n", + "\n", + "#Calculations:\n", + "Ia=P*10**3/(sqrt(3)*Va)\n", + "P1=If**2*Rf/10**3 #Loss in field winding(kW)\n", + "P2=3*Ia**2*Ra/10**3 #Loss in armature(kW)\n", + "Pl=(Pc+Pw+Ps+P1+P2)\n", + "Pi=Pin*pf+P1\n", + "Po=Pi-Pl\n", + "eff=(Po/Pi)*100\n", + "\n", + "#Results:\n", + "print \"Efficiency of the synchronous machine:\",round(eff,1),\"%\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Efficiency of the synchronous machine: 84.3 %\n" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.9, Page number: 287" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "import cmath\n", + "\n", + "#Variable declaration:\n", + "Xd=1 #Direct axis synchronus reactance(p.u)\n", + "Xq=0.60 #Quadrature axis synchronous reactance(p.u)\n", + "Va=1 #Terminal voltage(p.u)\n", + "pf=0.8 #Lagging power factor\n", + "Ia=0.8-1j*math.sin(math.acos(0.8)) #Line current(p.u)\n", + "\n", + "\n", + "#Calculations:\n", + "phy=-math.acos(pf)\n", + "E=Va+1j*Xq*Ia\n", + "delta=cmath.phase(E)\n", + "Id=abs(Ia)*math.sin(delta-phy)*cmath.exp(1j*(-pi/2+delta))\n", + "Iq=abs(Ia)*math.cos(delta-phy)*cmath.exp(1j*delta)\n", + "Eaf=Va+Xd*Id*1j+Xq*Iq*1j\n", + "\n", + "\n", + "#Results:\n", + "print \"Generated voltage:\",round(abs(Eaf),2),\"p.u Volt\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Generated voltage: 1.78 p.u Volt\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.11, Page number: 291" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from pylab import *\n", + "import cmath\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "P_rated=2000*746 #Rated power of motor(W)\n", + "Xs=1.95 #Synchronous reactance(ohm/phase)\n", + "Xd=1.95 #Direct axis synchronous reactance(ohm/ph)\n", + "Xq=1.40 #Quadrature axis synchronous reactance(ohm/ph)\n", + "pf=1 #Power factor of the machine\n", + "Vl=2300 #Line to line voltage(V)\n", + "\n", + "#Calculatons:\n", + "Va=float(Vl/sqrt(3)) #volt\n", + "Ia=float(P_rated/(Va*3)) #ampere\n", + "E1=Va-1j*Ia*Xq #From phasor diagram\n", + "delta=cmath.phase(E1) #power angle\n", + "Id=Ia*sin(abs(delta)) #direct axis current(A)\n", + "Eaf=abs(E1)+Id*(Xd-Xq)\n", + "r=symbols('r')\n", + "def P(r): #Process for finding maximum power\n", + " return Eaf*Va*sin(r)/Xd + Va**2*(Xd-Xq)*sin(2*r)/(2*Xd*Xq)\n", + "P1=diff(P(r),r)\n", + "#On differentiation,\n", + "#P1 = 1023732.58489791*cos(r) + 355250.305250306*(2*(cos(r))**2-1)\n", + "l = solve(1023732.58489791*cos(r) + 355250.305250306*(2*(cos(r))**2-1),r)\n", + "P_max = (P(round(l[0],5)))\n", + "\n", + "\n", + "#Results:\n", + "print \"Maximum mechanical power:\",math.ceil(3*P_max/10**3),\"kW,3-phase\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Maximum mechanical power: 3236.0 kW,3-phase\n" + ] + } + ], + "prompt_number": 1 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter7-checkpoint.ipynb b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter7-checkpoint.ipynb new file mode 100755 index 00000000..c5e8eebf --- /dev/null +++ b/ELECTRIC_MACHINERY/.ipynb_checkpoints/chapter7-checkpoint.ipynb @@ -0,0 +1,473 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 7: DC Machines" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.1, Page number: 371" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Vt=[128, 124] #Terminal voltage(V)\n", + "Ea=125 #Generated emf(V)\n", + "Ra=0.02 #Armature resistance(ohm)\n", + "n=3000 #rpm\n", + "\n", + "\n", + "#Calculations:\n", + "#For 128 V\n", + "Ia1=(Vt[0]-Ea)/Ra\n", + "Pin1=Vt[0]*Ia1\n", + "Pe1=Ea*Ia1\n", + "wm=3000*2*pi/60\n", + "Tmech1=Ea*Ia1/wm\n", + "\n", + "#for 124 V\n", + "Ia2=(-Vt[1]+Ea)/Ra\n", + "Pin2=Vt[1]*Ia2\n", + "Pe2=Ea*Ia2\n", + "Tmech2=Ea*Ia2/wm\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"(a) Armature current:\",Ia1,\"A\",\"\\n Terminal power:\",Pin1/10**3,\"kW\"\n", + "print \" Electromagnetic power:\",round(Pe1/10**3,2),\"kW\"\n", + "print \" Torque:\",round(Tmech1,1),\"Nm\"\n", + "\n", + "print \"(b) Armature current:\",Ia2,\"A\",\"\\n Terminal power:\",Pin2/10**3,\"kW\"\n", + "print \" Electromagnetic power:\",round(Pe2/10**3,2),\"kW\",\n", + "print \"\\n Torque:\",round(Tmech2,1),\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Armature current: 150.0 A \n", + " Terminal power: 19.2 kW\n", + " Electromagnetic power: 18.75 kW\n", + " Torque: 59.7 Nm\n", + "(b) Armature current: 50.0 A \n", + " Terminal power: 6.2 kW\n", + " Electromagnetic power: 6.25 kW \n", + " Torque: 19.9 Nm\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.2, Page number: 372" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Vt=123 #terminal voltage(V)\n", + "Pt=21.9 #Terminal power(kW)\n", + "Ra=0.02 #ohm\n", + "Eao=125 #generated voltage(V) at 3000rpm\n", + "no=3000 #rpm\n", + "\n", + "\n", + "#calculations:\n", + "Ia=Pt*10**3/Vt\n", + "Ea=Vt-Ia*Ra\n", + "n=(Ea/Eao)*no\n", + "\n", + "#Results:\n", + "print \"Speed of motor:\",round(n,0),\"rpm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Speed of motor: 2867.0 rpm\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.3, Page number: 376" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Il=400 #Armature current(A)\n", + "If=4.7 #Field current(A)\n", + "Ns=3 #series turns per pole\n", + "Nf=1000 #shunt field turns per pole\n", + "Eao=274 #at Ia=0,(V)\n", + "n=1150 #speed of motor(rpm)\n", + "no=1200 #rated speed(rpm) \n", + "Ra=0.025 #armature resistance(ohm)\n", + "Rs=0.005 #series field resistance(ohm)\n", + "\n", + "\n", + "#Calculations:\n", + "Is=Il+If\n", + "GM=If+(Ns/Nf)*Is #for graphical analysis\n", + "Ea=(n/no)*Eao\n", + "Vt=Ea-Is*(Ra+Rs)\n", + "\n", + "#Results:\n", + "print \"Terminal voltage at rated terminal current:\",round(Vt,0),\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Terminal voltage at rated terminal current: 250.0 V\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.4, Page number: 377" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Il=400 #Armature current(A)\n", + "If=4.7 #Field current(A)\n", + "Ns=3 #series turns per pole\n", + "Nf=1000 #shunt field turns per pole\n", + "Eao=261 #at Ia=400 A,(V)\n", + "n=1150 #speed of motor(rpm)\n", + "no=1200 #rated speed(rpm) \n", + "Ra=0.025 #armature resistance(ohm)\n", + "Rs=0.005 #series field resistance(ohm)\n", + "\n", + "\n", + "#Calculations:\n", + "Ea=(n/no)*Eao\n", + "Vt=Ea-(Il+If)*(Ra+Rs)\n", + "\n", + "#Results:\n", + "print \"Terminal voltage:\", round(Vt,0), \"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Terminal voltage: 238.0 V\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.5, Page number: 378" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Il=400 #Armature current(A)\n", + "If=4.7 #Field current(A)\n", + "Ns=3 #series turns per pole\n", + "Nf=1000 #shunt field turns per pole\n", + "Eao=269 #at Ia=400 A,(V)\n", + "n=1150 #speed of motor(rpm)\n", + "no=1200 #rated speed(rpm) \n", + "Ra=0.025 #armature resistance(ohm)\n", + "Rs=0.007 #series field resistance(ohm)\n", + "\n", + "#Calculations:\n", + "Is=Il+If\n", + "GM=If+(Ns/Nf)*Is #for graphical analysis\n", + "Ea=(n/no)*Eao\n", + "Vt=Ea-Is*(Ra+Rs)\n", + "\n", + "#Results:\n", + "print \"Terminal voltage at rated terminal current:\",round(Vt,0),\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Terminal voltage at rated terminal current: 245.0 V\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.6, Page number: 381" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "\n", + "#Variable declaration:\n", + "Ns=4 #Series field turns\n", + "Nf=1000 #Shunt field turns\n", + "Vt=250 #Full load voltage(V)\n", + "#for part (a):\n", + "Ia=400 #Armature current(A)\n", + "Ra=0.025 #Armature resistance(ohm)\n", + "\n", + "#for part (b):\n", + "Rs=0.005 #Added sries resistance(ohm)\n", + "Vo=250 #No load voltage(V)\n", + "If=5 #field current at full load(A)\n", + "\n", + "\n", + "#Calculations & Results:\n", + "\n", + "#for part (a)\n", + "V1=Ia*Ra\n", + "\n", + "#for part (b):\n", + "Ia1=Ia+If\n", + "Rs,Rd=symbols('Rs Rd') #Rd= diverter resistance(ohm)\n", + "Rp=Rs*Rd/(Rs+Rd) # -------(i)\n", + "Is=Ia1*(Rd/(Rs+Rd))\n", + "Inet=If+(Ns/Nf)*Is\n", + "Ea=Vt+Ia*(Ra+Rp) # -------(ii)\n", + "\n", + "#from equation (ii)\n", + "Rp=Rs(Inet-5.0)/1.62 \n", + "R_d=0.0082 #R_d=Rd(say), using (i)\n", + "print \"(a) The operating terminal voltage = 205 V\", Inet\n", + "print \"(b) Rd =\", R_d,\"ohm\"\n", + "print \"\\tHence, by this process, resistance across the series field\" \n", + "print \"\\t(referred to as a series-field diverter) can be adjusted \"\n", + "print \"\\tto produce the desired performance. \"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) The operating terminal voltage = 205 V 1.62*Rd/(Rd + Rs) + 5\n", + "(b) Rd = 0.0082 ohm\n", + "\tHence, by this process, resistance across the series field\n", + "\t(referred to as a series-field diverter) can be adjusted \n", + "\tto produce the desired performance. \n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.7, Page number: 383" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + " \n", + "#Variable declaration:\n", + "Ia=400 #Armature current(A)\n", + "n1=1200 #rpm\n", + "n2=1100 #rpm\n", + "Ra=0.025 #armature resistance(ohm) \n", + "Eo=250 #no load armature voltage(V)\n", + "del_n=1.5 #fractional winding added\n", + "N=1000 #Total windings\n", + "\n", + "\n", + "#Calculations:\n", + "#for part(a):\n", + "#point corresponding on the no load saturation curve is :\n", + "Eao=Eo*(n1/n2)\n", + "#using Eao value in curve, value of If is found to be:\n", + "If=5.90 #Field current(A)\n", + "Ea=Eo-Ia*Ra\n", + "#From Fig. 7.14\n", + "Ea1=261\n", + "n=n1*(Ea/Ea1)\n", + "Pe=Ea*Ia\n", + "Pl=2000 #No load Rotational loss(W)\n", + "Po=(Pe-Pl)/(1+0.01)\n", + "\n", + "#for part (b):\n", + "If1=If+del_n/N\n", + "#From Fig. 7.14 the corresponding value of Ea at 1200 r/min would be 271 V.\n", + "Ea2=271 #volts\n", + "n22=n1*(Ea/Ea2)\n", + "\n", + "\n", + "#Results:\n", + "print \"Part(a):\"\n", + "print \"Required speed =\",round(n),\"r/min\"\n", + "print \"Output power =\", round((Po/746),1),\"hp\"\n", + "print \"\\nPart (b):\"\n", + "print \"Required speed =\",round(n22),\"r/min\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Part(a):\n", + "Required speed = 1103.0 r/min\n", + "Output power = 124.8 hp\n", + "\n", + "Part (b):\n", + "Required speed = 1063.0 r/min\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.9, Page number: 389" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "V1=50 #terminal voltage(V)\n", + "Ia=1.25 #Armature current(A)\n", + "Ra=1.03 #Armature resistance(ohm)\n", + "n1=2100 #speed at 50V(rpm)\n", + "V2=48 #terminal voltage at 1700 rpm (V)\n", + "n2=1700 #speed at 48 V(rpm)\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "#for (a):\n", + "Ea1=V1-Ia*Ra\n", + "wm1=n1*2*pi/60\n", + "Km=round(Ea1/wm1,2)\n", + "\n", + "#for part(b):\n", + "Prot=Ea1*Ia\n", + "\n", + "#for part(c:)\n", + "wm2=n2*2*pi/60\n", + "Ea2=Km*wm2\n", + "Ia2=(V2-Ea2)/Ra\n", + "Pmech=Ea2*Ia2\n", + "Pshaft=Pmech-Prot\n", + "\n", + "#Results:\n", + "print \"(a) Torque constant:\",round(Km,2),\"V/(rad/s)\"\n", + "print \"(b) No-load rotational losses of the motor:\",round(Prot,0),\"W\"\n", + "print \"(c) The power output of the motor:\",round(Pshaft,2),\"W\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Torque constant: 0.22 V/(rad/s)\n", + "(b) No-load rotational losses of the motor: 61.0 W\n", + "(c) The power output of the motor: 275.05 W\n" + ] + } + ], + "prompt_number": 3 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/README.txt b/ELECTRIC_MACHINERY/README.txt new file mode 100755 index 00000000..4b574a6d --- /dev/null +++ b/ELECTRIC_MACHINERY/README.txt @@ -0,0 +1,10 @@ +Contributed By: SANTOSH BARNWAL +Course: be +College/Institute/Organization: BIRLA INSTITUTE OF TECHNOLOGY MESRA +Department/Designation: ELECTRICAL & ELECTRONICS +Book Title: ELECTRIC MACHINERY +Author: A. E. Fitzgerald, Charles Kingsley, Jr., Stephen D. Umans +Publisher: McGraw-Hill, New York +Year of publication: 2003 +Isbn: 0-07-112193-5 +Edition: Sixth Edition
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter1.ipynb b/ELECTRIC_MACHINERY/chapter1.ipynb new file mode 100755 index 00000000..0e747060 --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter1.ipynb @@ -0,0 +1,568 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<h1>Chapter 1:Introduction to Magnetic Circuits<h1>" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.1, Page number: 9" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "#Variable declaration:\n", + "Ac=9 #Cross-sectional area of the core(cm**2)\n", + "Ag=9 #Cross-sectional area of the air-gap(cm**2)\n", + "g=0.050 #Air-gap length(cm) \n", + "lc=30 #Mean Length of the core(cm)\n", + "N=500 #No. of windings\n", + "ur=70000 #Relative permeability of the core material\n", + "Bc=1.0 # Magnetic Flux Density of the core(T)\n", + "uo=4*pi*10**-7 #Permeability of free space\n", + "\n", + "#Calculation\n", + "Rc=lc*10**-2/((ur*uo*Ac)*10**-4)\n", + "Rg=g*10**-2/((uo*Ag)*10**-4)\n", + "Q=Bc*Ac*10**-4\n", + "i=Q*(Rc+Rg)/N\n", + "\n", + "#Results\n", + "print \"a.Reluctance of the core,Rc:\",round(Rc,2), \"A.turns/Wb\" \n", + "print \" Reluctance of the air-gap,Rg:\", round(Rg,2), \"A.turns/Wb\"\n", + "print \"b.The flux, Q:\", round(Q,4), \"Wb\"\n", + "print \"c.The current,i:\", round(i,2), \"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "a.Reluctance of the core,Rc: 3789.4 A.turns/Wb\n", + " Reluctance of the air-gap,Rg: 442097.06 A.turns/Wb\n", + "b.The flux, Q: 0.0009 Wb\n", + "c.The current,i: 0.8 A\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.2, Page number: 10" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "#Variable declaration:\n", + "I=10 #Current in the coil(A)\n", + "N=1000 #No of turns in the rotor\n", + "g=1 #Air gap length(cm)\n", + "Ag=2000 #Cross-section of the air-gap(cm**2)\n", + "uo=4*pi*10**-7 #Permeability of free space\n", + "\n", + "#Calculation:\n", + "Q=(N*I*uo*Ag*10**-4)/(2*g*10**-2)\n", + "Bg=round(Q,2)/(Ag*10**-4)\n", + "\n", + "#Results\n", + "print \"The air-gap flux, Q:\", round(Q,2), \"Wb\"\n", + "print \"The flux density, Bg:\", round(Bg,4), \"T\"\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The air-gap flux, Q: 0.13 Wb\n", + "The flux density, Bg: 0.65 T\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.4, Page number: 13" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "#Variable declaration\n", + "lc=0.3 #length of the core(cm)\n", + "ur1=72300 #Relative permeablity for case(a)\n", + "ur2=2900 #Relative permeablity for case(b)\n", + "Ac=9 #Cross-section of the core(cm**2)\n", + "Rg=4.42*10**5 #Reluctance of the air-gap(A.turns/Wb)\n", + "N=500 #No of coil turns\n", + "uo=4*pi*10**-7 #Permeability of free space(H/m)\n", + "\n", + "#Calculations:\n", + "Rt1=(lc/(ur1*uo*Ac*10**-4))+Rg\n", + "L1=N**2/Rt1\n", + "Rt2=(lc/(ur2*uo*Ac*10**-4))+Rg\n", + "L2=N**2/Rt2\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"(a)Inductance,L:\",round(L1,2),\"H\"\n", + "print \"(b)Inductance,L:\",round(L2,2),\"H\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a)Inductance,L: 0.56 H\n", + "(b)Inductance,L: 0.47 H\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.5, Page number: 15" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from pylab import *\n", + "from matplotlib import *\n", + "from math import *\n", + "%pylab inline\n", + "#Variable declaration:\n", + "Ac=9e-4 #Cross-section of the core(m)\n", + "Ag=9e-4 #Cross-section of the air-gap(m)\n", + "g=5e-4 #Air-gap length(m)\n", + "lc=0.3 #Mean length of the core(m)\n", + "N=500 #No. of turns of the core(m)\n", + "uo=4*pi*10**-7 #Permeability of free space(H/m)\n", + "\n", + "#Calculations:\n", + "Rg=g/(uo*Ag) #Reluctance of the air-gap(A.turns/Wb)\n", + "ur=[0]*200 #Initialising array\n", + "L=[0]*200\n", + "\n", + "for n in range(1,101,1):\n", + " ur[n-1]=100+(10000-100)*(n-1)/100\n", + " Rc=lc/(ur[n-1]*uo*Ac) #Reluctance of the core(A.turns/Wb)\n", + " Rtot=Rg+Rc\n", + " L[n-1]=(N**2)/Rtot #Inductance(H)\n", + " \n", + "\n", + "#Results:\n", + "print \"The reqired plot is shown below:\"\n", + "plot(ur, L,'g.')\n", + "xlabel('Core relative permeability, ur')\n", + "ylabel('Inductance,L (H) ')\n", + "title('plot of inductance vs. relative permeability for Example 1.5.')\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "The reqired plot is shown below:\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['streamplot', 'rc', 'tri', 'axes', 'sinh', 'legend', 'trunc', 'rc_context', 'figure', 'f', 'quiver', 'tan', 'axis', 'cosh', 'degrees', 'radians', 'fmod', 'expm1', 'ldexp', 'linalg', 'exp', 'draw_if_interactive', 'text', 'random', 'colors', 'stackplot', 'frexp', 'ceil', 'contour', 'isnan', 'copysign', 'cos', 'fft', 'tanh', 'colorbar', 'fabs', 'sqrt', 'rcdefaults', 'hypot', 'table', 'power', 'gamma', 'log', 'log10', 'info', 'log1p', 'floor', 'modf', 'test', 'pi', 'isinf', 'e', 'sin']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEZCAYAAABrUHmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiKDMWkQEZHBmDSIiMhgTBpERGSw/wfGiiSfSPgEbwAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x31b54d0>" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.6, Page number: 19" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "Bc=1.0 #Magnetic field induction in the core\n", + "w=377 #Angular frequency of magnetic field(rad/s)\n", + "Rc=3791.33 #Reluctance of the core(A.turns/Wb)\n", + "Rg=442321.3 #Reluctance of the air-gap(A.turns/Wb)\n", + "N=500 #No. of windings\n", + "i=0.80 #Current in the coil\n", + "Ac=9*10**-4 #Cross-section of the core\n", + "\n", + "\n", + "#Calculations:\n", + "L=N**2/(Rc+Rg)\n", + "W=(1./2)*L*i**2\n", + "t = symbols('t')\n", + "Bc = 1.0*sin(w*t)\n", + "e=N*Ac*diff(Bc,t)\n", + "\n", + "#Results:\n", + "print \"The Inductance, L:\", round(L,2), \"H\"\n", + "print \"The magntic stored energy, W:\", round(W,2), \"J\"\n", + "print \"Induced voltage, e:\",e,\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The Inductance, L: 0.56 H\n", + "The magntic stored energy, W: 0.18 J\n", + "Induced voltage, e: 169.65*cos(377*t) V\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.7, Page number: 22" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "#Variable declaration:\n", + "Bc=1 #Magnetic field in the core\n", + "Hc=11 #Magnetising force(A.turns/m)\n", + "lc=0.3 #length of the core(m)\n", + "N=500 #No of windings\n", + "g=0.050 #Air-gap length(cm)\n", + "uo=4*pi*10**-7 #Permeability of free space(H/m)\n", + "\n", + "\n", + "#Calculation:\n", + "Fc=Hc*lc #mmf drop for the core path(A.turns)\n", + "Fg=Bc*g*10**-2/uo #mmf drop across the air gap(A.turns)\n", + "i=(Fc+Fg)/N\n", + "\n", + "\n", + "#Results:\n", + "print \"The required current,i:\" ,round(i,2) ,\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The required current,i: 0.8 A\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.8, Page number: 28" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "N=200 #No. of turns\n", + "Ac=4 #Cross-section of the core(in**2)\n", + "w=377 #Angular frequency of the magnetic field(rad/s)\n", + "Hm=36 #Max value magnetising force(A.turns/m)\n", + "Pc=1.2 #Core loss density(W/kg)\n", + "\n", + "\n", + "#Calculations:\n", + "t=symbols('t')\n", + "Bc=1.5*sin(w*t)\n", + "e=(round(N*Ac*0.94/(39.4**2),2)*diff(Bc,t))\n", + "Erms=275*0.707\n", + "lc=(6+6+8+8)/39.4 #Mean length of the core(m)\n", + "I=Hm*lc/N\n", + "Vc=4*0.94*28 #Core volume(m**3)\n", + "Wc=105.5*(2.54**3)*7.65*10**-3 #Core weight(kg)\n", + "Pa=1.5*13.2 #Watts per Kg\n", + "Irms=Pa/Erms #Current (A)\n", + "Pct=Pc*Wc #Total core loss(W)\n", + "\n", + "\n", + "#Results:\n", + "print \"The applied voltage,e:\", e, \"V\"\n", + "print \"The peak current,I:\", round(I,2), \"A\"\n", + "print \"The total rms current. Irms:\", round(Irms,2), \"A\"\n", + "print \"Total Core loss, Pct:\",round(Pct,2),\"W\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The applied voltage,e: 271.44*cos(377*t) V\n", + "The peak current,I: 0.13 A\n", + "The total rms current. Irms: 0.1 A\n", + "Total Core loss, Pct: 15.87 W\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.9, Page number: 32" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "from math import *\n", + "\n", + "#Variable Declaration:\n", + "g=0.2 #air-gap length(cm)\n", + "lm=1.0 #length of magnetic section(cm)\n", + "Am=4 #Cross-section of the core(cm**2)\n", + "Ag=4 #Cross-section of the air-gap(cm**2)\n", + "\n", + "#Constants used:\n", + "uo=4*pi*10**-7 #Permeability of free space(H/m)\n", + "\n", + "#Calculations:\n", + "Hm=symbols('Hm')\n", + "def Bg(Hm):\n", + " return -uo*Ag*lm*Hm/(Am*g) \n", + "\n", + "Hm1=-49*10**3 #Coercivity of ALNICO 5 (A/m)\n", + "Hm2=-6 #Coercivity of M-5 electrical steel (A/m) \n", + "\n", + "\n", + "#Results:\n", + "print \"Flux Density of air gap:\", round(Bg(Hm1),2),\"T\"\n", + "print \"\\nFlux Density of air gap:\", round(Bg(Hm2*10**4),2),\"gauss\"\n", + "print \"\\nwhere value of Hm for different material.\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Flux Density of air gap: 0.31 T\n", + "\n", + "Flux Density of air gap: 0.38 gauss\n", + "\n", + "where value of Hm for different material.\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.10, Page number: 34" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Ag=2 #Cross-section of air-gap(cm**2) \n", + "Bg=0.8 #Air-gap flux density(t)\n", + "Bm=1.0 #Core-flux density(T)\n", + "Hm=-40 #Magnetising force in the core(kA/m)\n", + "uo=4*pi*10**-7 #permeability of free space(H/m)\n", + "g=0.2 #Air-gap length(cm)\n", + "\n", + "#Calculations:\n", + "Am=Ag*Bg/Bm\n", + "lm=-g*Bg/(Hm*uo*10**3)\n", + "Vm=Am*lm\n", + "\n", + "\n", + "#Results:\n", + "print \"The minimum magnet volume,Vm:\",round(Vm,2),\"cm**3\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The minimum magnet volume,Vm: 5.09 cm**3\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 1.11, Page number: 39" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "from math import *\n", + "\n", + "#Variable Declaration:\n", + "Am = 2 #magnetic material cros-section(cm^2)\n", + "g=0.2 #air gap length(cm)\n", + "uo=4*pi*10**-7 #permeability of free space(H/m)\n", + "N=100 #No. of windings\n", + "\n", + "#Calculations and results:\n", + "#for part (a)\n", + "Bma = 1.0 #Tesla\n", + "Hma = - 4 #kA/m\n", + "Ag1 = 2 #cm**2\n", + "Ag2 = 4 #cm**2\n", + "\n", + "lm=g*(Am/Ag1)*(Bma/(-uo*Hma*10**4))\n", + "print \"(a) The Requied magnet length = \",round(lm,2),\"cm\"\n", + "\n", + "\n", + "#for part (b):\n", + "i,Hm=symbols('i Hm')\n", + "Bm=-uo*(Ag1/Am)*(lm/g)*Hm+(uo*N/g)*(Ag1/Am)*i\n", + "H_max=200 #kA/m\n", + "B_max=2.1 #Tesla\n", + "i_max=(B_max+2.50*10**-5*H_max)/(6.28*10**-2)\n", + "\n", + "print \"(b) Thus with the air-gap area set to 2 cm^2,\"\n", + "print \" increasing the current to i_max = 45.2 A and then reducing\"\n", + "print \" it to zero will achieve the desired magnetization.\"\n", + "\n", + "#for part (c):\n", + "Bm1=1.00 #Tesla\n", + "Bm2=1.08 #Tesla\n", + "Bg1=(Am/Ag1)*Bm1\n", + "Bg2=(Am/Ag2)*Bm2\n", + "print \"(c) The flux densities when plunger moves at two extremes are:\"\n", + "print \" Bg1 =\",Bg1,\"T and Bg2 =\",Bg2,\"T\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) The Requied magnet length = 3.98 cm\n", + "(b) Thus with the air-gap area set to 2 cm^2,\n", + " increasing the current to i_max = 45.2 A and then reducing\n", + " it to zero will achieve the desired magnetization.\n", + "(c) The flux densities when plunger moves at two extremes are:\n", + " Bg1 = 1.0 T and Bg2 = 0.54 T\n" + ] + } + ], + "prompt_number": 12 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter10.ipynb b/ELECTRIC_MACHINERY/chapter10.ipynb new file mode 100755 index 00000000..537bfc9f --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter10.ipynb @@ -0,0 +1,575 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 10: Introduction to Power Electronics" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.5, Page number: 508" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from pylab import *\n", + "import numpy as np\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "w=2*pi*60 #frequency of voltage(Hz)\n", + "R=10 #ohm\n", + "C=0.01 #F\n", + "Vo=120*sqrt(2) #maximum voltage(V)\n", + "Nmax=800\n", + "tau=R*C #time constant(s)\n", + "\n", + "#Calculations:\n", + "# diode = 1 when rectifier bridge is conducting\n", + "\n", + "diode=1\n", + "t=[0]*801\n", + "vs=[0]*801\n", + "vrect=[0]*801\n", + "vR=[0]*801\n", + "iB=[0]*801\n", + "\n", + "t=[0]*801\n", + "for n in range(1,Nmax+2,1):\n", + " t[n-1] = (2.5*pi/w)*(n-1)/Nmax\n", + " vs[n-1]=Vo*math.cos(w*t[n-1])\n", + " vrect[n-1]=abs(vs[n-1])\n", + "#if the rectifier bridge is ON:\n", + " if diode==1:\n", + " vR[n-1]=vrect[n-1]\n", + " if (w*t[n-1])<=(pi/2):\n", + " iB[n-1]=vR[n-1]-Vo*C*w*math.sin(w*t[n-1])\n", + " elif (w*t[n-1])<=3*pi/2:\n", + " iB[n-1]=vR[n-1]/R+Vo*C*w*math.sin(w*t[n-1])\n", + " else:\n", + " iB[n-1]=vR[n-1]/R-Vo*C*w*math.sin(w*t[n-1])\n", + " if iB[n-1]<0:\n", + " diode=0\n", + " toff=t[n-1]\n", + " Voff=vrect[n-1]\n", + " else:\n", + " vR[n-1]=Voff*exp(-(t[n-1]-toff/tau))\n", + " iB[n-1]=0\n", + " if (vrect[n-1]-vR[n-1])>0:\n", + " diode=1\n", + "\n", + "\n", + "\n", + "#Results:\n", + "iR=(1/R)*np.array(vR)\n", + "plot(1000*np.array(t),vR)\n", + "xlabel('time [msec]')\n", + "ylabel('voltage [V]')\n", + "xlim(0,22)\n", + "ylim(0,180)\n", + "plot(1000*np.array(t),vrect,'--')\n", + "grid()\n", + "print \"The required plots are shown below:\"\n", + "show()\n", + "plot(1000*np.array(t),iR)\n", + "xlabel('time [msec]')\n", + "ylabel('source current [A]')\n", + "xlim(0 ,22)\n", + "ylim(-50,250) \n", + "plot(1000*np.array(t),1.5*np.array(iB),'--')\n", + "grid()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "The required plots are shown below:" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEPCAYAAACk43iMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOE6H/T8o9Q6nwzgdWAAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x7f054c060710>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEPCAYAAACgFqixAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEREB+P84YbaUtpWtuAAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x2986d90>" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.6, Page number: 522" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "w=2*pi*60 #Angular freq of voltage(rad/sec)\n", + "Vo=230*sqrt(2) #volt\n", + "R=5.6 #Resistance(ohm)\n", + "\n", + "#Calculations:\n", + "Ls=[0]*101\n", + "tc=[0]*101\n", + "Idc=[0]*101\n", + "for n in range(1,101,1):\n", + " Ls[n-1]=n*10**-3\n", + " Idc[n-1]=2*Vo/(pi*R+2*w*Ls[n-1])\n", + " tc[n-1]=(1/w)*acos(1-(2*Idc[n-1]*w*Ls[n-1])/Vo)\n", + "\n", + "#Results:\n", + "plot(1000*np.array(Ls),Idc,'g.')\n", + "xlabel('Commutating inductance Ls [mH]')\n", + "ylabel('Idc [A]')\n", + "title('Load current,Idc vs Commutating inductance,Ls')\n", + "show()\n", + "plot(1000*np.array(Ls),1000*np.array(tc),'g.')\n", + "xlabel('Commutating inductance L [mH]')\n", + "ylabel('tc [msec]')\n", + "title('Commutating Inductance,Ls vs time,tc')\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['fmod', 'sinh', 'trunc', 'tan', 'gamma', 'cosh', 'radians', 'modf', 'expm1', 'ldexp', 'linalg', 'random', 'frexp', 'ceil', 'isnan', 'copysign', 'cos', 'degrees', 'tanh', 'fabs', 'sqrt', 'hypot', 'power', 'log', 'log10', 'info', 'log1p', 'floor', 'fft', 'pi', 'exp', 'isinf', 'e', 'sin']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEZCAYAAACXRVJOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x2c18b90>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEZCAYAAACQK04eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x2981fd0>" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.7, Page number: 528" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "R=12.5*10**-3 #ohm\n", + "L=1.2 #H\n", + "Vo=15 #volt\n", + "w=120*pi #angular freq(Hz)\n", + "Idc=35 #DC current(A)\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "theta=[0]*1301\n", + "t=[0]*1301\n", + "vL=[0]*1301\n", + "vs=[0]*1301\n", + "\n", + "Vdc_a=R*Idc #Dc voltage(V)\n", + "P=Vdc_a*Idc #Power\n", + "alpha_da = acos(pi*R*Idc/(2*Vo)) ; #delay angle\n", + "for n in range(1,1301,1): #loop for calculating load voltage\n", + " theta[n-1]=2*pi*(n-1)/1000\n", + " t[n-1]=theta[n-1]/w\n", + " vs[n-1]=Vo*sin(theta[n-1])\n", + " if theta[n-1]<alpha_da:\n", + " vL[n-1]=-vs[n-1]\n", + " elif (theta[n-1]<pi+alpha_da):\n", + " vL[n-1]=-vs[n-1]\n", + " elif theta[n-1]<2*pi+alpha_da:\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<3*pi+alpha_da:\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<4*pi+alpha_da:\n", + " vL[n-1]=-vs[n-1]\n", + " else:\n", + " vL[n-1]=vs[n-1]\n", + "\n", + "figure(1)\n", + "plot(1000*np.array(t),vL,'g.')\n", + "xlabel('time [msec]')\n", + "ylabel('Load voltage [V]')\n", + "grid()\n", + "show()\n", + "\n", + "\n", + "#part(b):\n", + "alpha_db=0.9*pi #delay angle\n", + "Vdc_b=(2*Vo/pi)*cos(alpha_db) #new dc voltage(V)\n", + "tau=L/R #time constant(s)\n", + "imo=Idc #Initial curent(A)\n", + "tzero=-tau*log((-Vdc_b/R)/(imo-Vdc_b/R))\n", + "for n in range(1,1301,1):\n", + " theta[n-1]=2*pi*(n-1)/1000\n", + " t[n-1]=theta[n-1]/w\n", + " vs[n-1]=Vo*sin(theta[n-1])\n", + " if theta< alpha_db:\n", + " vL[n-1]=-vs[n-1]\n", + " elif (theta[n-1]<pi+alpha_db):\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<2*pi+alpha_db:\n", + " vL[n-1]=-vs[n-1]\n", + " elif theta[n-1]<3*pi+alpha_db:\n", + " vL[n-1]=vs[n-1]\n", + " elif theta[n-1]<4*pi+alpha_db:\n", + " vL[n-1]=-vs[n-1]\n", + " else:\n", + " vL[n-1]=vs[n-1]\n", + "\n", + "#Results:\n", + "figure(2)\n", + "plot (1000*np.array(t), vL,'g.')\n", + "xlabel('time [msec] ')\n", + "ylabel('Load voltage [V]')\n", + "print \"part (a):\"\n", + "print \"\\n Vdc_a=\",round(1000*Vdc_a,2),\"mV\"\n", + "print \"\\n Power=\",round(P),\"W\" \n", + "print \"\\n alpha_d=\",round((180/pi)*alpha_da,1),\"degrees\"\n", + "print \"\\n part (b):\"\n", + "print \"\\n alpha_d=\",round((180/pi)*alpha_db,1),\"degrees\" \n", + "print \"\\n Vdc_b=\",round(Vdc_b,1),\"V\"\n", + "print \"\\n Current will reach zero at\",round(tzero,1),\"sec\"\n", + "grid()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['fmod', 'cosh', 'sinh', 'trunc', 'tan', 'gamma', 'degrees', 'radians', 'sin', 'expm1', 'ldexp', 'isnan', 'frexp', 'ceil', 'copysign', 'cos', 'tanh', 'fabs', 'sqrt', 'hypot', 'log', 'log10', 'pi', 'log1p', 'floor', 'modf', 'exp', 'isinf', 'e']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhNj0/wELMsFZawmOEgAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x1e16bd0>" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "part (a):\n", + "\n", + " Vdc_a= 437.5 mV\n", + "\n", + " Power= 15.0 W\n", + "\n", + " alpha_d= 87.4 degrees\n", + "\n", + " part (b):\n", + "\n", + " alpha_d= 162.0 degrees\n", + "\n", + " Vdc_b= -9.1 V\n", + "\n", + " Current will reach zero at 4.5 sec\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCHEqf8PfsUAKezuFf4AAAAASUVORK5CYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x2983e10>" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.8, Page number: 533" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "import math\n", + "\n", + "#Variable declaration:\n", + "f=60 #Hz\n", + "Vrms=35 #rms voltage of waveform\n", + "Ra=3.5 #Armature resistance(ohm)\n", + "La=0.175 #H\n", + "no=8000 #No load speed(r/min)\n", + "Va=50 #armature voltage(V)\n", + "\n", + "#Calculations:\n", + "Edc,alphad=symbols('Edc alphad')\n", + "Vdc=Edc #at no load, Vdc=Edc\n", + "Edc=round(float(2*sqrt(2)*(Vrms/math.pi)),2)*cos(alphad)\n", + "n=Edc*float(no/50)\n", + "\n", + "#Results:\n", + "print \"Speed at no-load =\",n,\" r/min (where 0 <= alphad <= pi/2)\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Speed at no-load = 5041.6*cos(alphad) r/min (where 0 <= alphad <= pi/2)\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.9, Page number: 537" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Vll_rms=460 #rms voltage,line-to-line(V)\n", + "R=68 #resistance of load\n", + "Im=2.5 #magnet current(A)\n", + "\n", + "#Calculations:\n", + "Vdc_max=3*sqrt(2)*Vll_rms/pi\n", + "Idc_max=Vdc_max/R\n", + "Vdc=Im*R\n", + "alpha=acos(pi*Vdc/(3*sqrt(3)*Vll_rms))\n", + "\n", + "#Results:\n", + "print \"(a) Maximum dc voltage:\",round(Vdc_max),\"V\"\n", + "print \"\\n Maximum dc current:\",round(Idc_max,1),\"V\"\n", + "print \"\\n(b) Delay angle alpha:\",round(math.degrees(round(alpha,1)),1),\"degrees\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Maximum dc voltage: 621.0 V\n", + "\n", + " Maximum dc current: 9.1 V\n", + "\n", + "(b) Delay angle alpha: 74.5 degrees\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.10, Page number: 541" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "T=20*10**-3 #Time period(sec) \n", + "p=4 #no. of poles\n", + "delta=0.44 #ON- time fraction\n", + "Vo=125 #DC supply voltage(V)\n", + "\n", + "\n", + "#Calculation:\n", + "fc=1/T\n", + "ns=(120*fc/p)\n", + "Va_peak=(4*Vo*sin(delta*pi))/pi\n", + "Vll_rms=sqrt(3/2)*Va_peak\n", + "\n", + "#Results:\n", + "print \"(a) Frequency:\",fc,\"Hz\"\n", + "print \"\\n Synchronous speed:\",ns,\"r/min\"\n", + "print \"\\n(b) Rms amplitude of line-to-line voltage:\",round(Vll_rms,0),\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Frequency: 50.0 Hz\n", + "\n", + " Synchronous speed: 1500.0 r/min\n", + "\n", + "(b) Rms amplitude of line-to-line voltage: 191.0 V\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 10.13, Page number: 547" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Vo=48 #Load voltage(V)\n", + "R=3.7 #Resistance of load(ohm)\n", + "L=.32 #Inductance of laad(H)\n", + "D=0.8 #Duty cycle\n", + "f=1000 #Hz\n", + "\n", + "#Calculations:\n", + "iL_avg=(2*D-1)*Vo/R\n", + "T=1/f\n", + "tau=L/R\n", + "iL_min=((-Vo/R)*(1-2*exp(-T*(1-D)/tau)+exp(-T/tau)))/(1-exp(-T/tau))\n", + "iL_max=(Vo/R)*(1-2*exp(-D*T/tau)+exp(-T/tau))/(1-exp(-T/tau))\n", + "\n", + "#since T/tau << 1, so using 10.32 in e.g. given.\n", + "del_iL=(2*Vo)*T*D*(1-D)/(R*tau)\n", + "\n", + "\n", + "#Results:\n", + "print \"Avg load current:\",round(iL_avg,2),\"A\"\n", + "print \"Minimum load current:\",round(iL_min,2),\"A\"\n", + "print \"Maximum load current\",round(iL_max,2),\"A\"\n", + "print \"Current ripple:\",round(del_iL,2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Avg load current: 7.78 A\n", + "Minimum load current: 7.76 A\n", + "Maximum load current 7.81 A\n", + "Current ripple: 0.05 A\n" + ] + } + ], + "prompt_number": 9 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter11.ipynb b/ELECTRIC_MACHINERY/chapter11.ipynb new file mode 100755 index 00000000..c9c095c8 --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter11.ipynb @@ -0,0 +1,990 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 11: Speed and Torque Control" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.1, Page number: 561" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Vdc=240 #DC supply (V)\n", + "D=0.75 #Duty cycle\n", + "Rf=187 #field resistance(ohm)\n", + "Lf=4.2 #field winding inductance(H)\n", + "T=1 #switching period(msec)\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "If=D*(Vdc/Rf)\n", + "tau=Lf/Rf #time constant(msec)\n", + "del_if=(2*Vdc/Rf)*(T/tau)*D*(1-D)\n", + "\n", + "\n", + "#Results:\n", + "print \"Avg field current:\",round(If,2),\"A\"\n", + "print \"Magnitude of currnet ripple:\",round(del_if,1),\"mA\"\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Avg field current: 0.96 A\n", + "Magnitude of currnet ripple: 21.4 mA\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.2, Page number: 563" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "n1=1800 #r/min\n", + "n2=3600 #r/min\n", + "Va=240 #terminal voltage(V)\n", + "Ifo=0.34 #No-load field current(A)\n", + "Ra=0.05 #Armature resistance(ohm)\n", + "Rsh=187 #Shunt field resistance(ohm)\n", + "\n", + "#Calculations:\n", + "wm=symbols('wm')\n", + "wm1=float(2*pi*n1/60)\n", + "wm2=float(2*pi*n2/60)\n", + "def Pload(wm):\n", + " return (22.4*(120*pi)**-3)*(wm)**3\n", + "\n", + "T1=Pload(wm1)*1000/wm1\n", + "T2=Pload(wm2)*1000/wm2\n", + "\n", + "Kf=Va/(Ifo*wm2)\n", + "def If(T,wm):\n", + " return (Va/(2*Kf*wm))*(1+sqrt(1-(4*wm*T*Ra)/Va**2))\n", + "\n", + "Rf1tot=round(Va/float(If(T1,wm1)))\n", + "Rf2tot=round(Va/float(If(T2,wm2)))\n", + "Rrh1=Rf1tot-Rsh\n", + "Rrh2=Rf2tot-Rsh\n", + "\n", + "\n", + "#Results:\n", + "print \"----------------------------------------------------------------\"\n", + "print \"r/min Tload[N.m] If[A] R(f)tot[ohm] Rrheostat[ohm]\"\n", + "print \"----------------------------------------------------------------\"\n", + "print n1,\"\\t \",round(float(T1),1),\"\\t\\t \",round(float(If(T1,wm1)),3),\"\\t\",Rf1tot,\"\\t \",Rrh1\n", + "print n2,\"\\t \",round(float(T2),1),\"\\t\\t \",round(float(If(T2,wm2)),3),\"\\t\",Rf2tot,\"\\t \",Rrh2\n", + "print \"----------------------------------------------------------------\"\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "----------------------------------------------------------------\n", + "r/min Tload[N.m] If[A] R(f)tot[ohm] Rrheostat[ohm]\n", + "----------------------------------------------------------------\n", + "1800 \t 14.9 \t\t 0.678 \t354.0 \t 167.0\n", + "3600 \t 59.4 \t\t 0.333 \t720.0 \t 533.0\n", + "----------------------------------------------------------------\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.3, Page number: 567" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Rf=109 #Field resistance(ohm)\n", + "Vf=300 #Rated field voltage(V)\n", + "Ra=0.084 #Armature resistance(ohm)\n", + "Kf=0.694 #Geometric constant(A.rad/sec)\n", + "\n", + "\n", + "#Calculations:\n", + "If=Vf/Rf #Resulting field current(A)\n", + "w_rated=2500*(pi/30) #Rated speed(rad/sec)\n", + "P_rated=100*746 #Watts\n", + "T_rated=P_rated/w_rated #Nm\n", + "Va=[0]*102\n", + "NoLoadRPM=[0]*102\n", + "FullLoadRPM=[0]*102 \n", + "for n in range(1,102,1):\n", + " Va[n-1]=250*(1+(n-1)/100)\n", + " T=0 #Zero torque\n", + " w=(Va[n-1]-T*Ra/(Kf*If))/(Kf*If)\n", + " NoLoadRPM[n-1]=w*30/pi\n", + " T=T_rated\n", + " w=(Va[n-1]-T*Ra/(Kf*If))/(Kf*If)\n", + " FullLoadRPM[n-1]=w*30/pi\n", + "\n", + "print\"The plot is as shown:\"\n", + "plot(Va,NoLoadRPM)\n", + "plot(Va[20] ,NoLoadRPM[20] ,'r+')\n", + "plot (Va[50] , NoLoadRPM[50] , 'r+')\n", + "plot (Va[80] ,NoLoadRPM[80] , 'r+')\n", + "plot (Va, FullLoadRPM,'.')\n", + "plot (Va[20] ,FullLoadRPM[20] ,'o')\n", + "plot (Va[50] , FullLoadRPM[50] , ' o' )\n", + "plot (Va[80] , FullLoadRPM[80] ,'o' )\n", + "title('Speed vs Armature voltage')\n", + "xlabel('Armature voltage [V] ')\n", + "ylabel('Speed [r/min] ')\n", + "annotate('+ = Zero torque',xy=(270,2300))\n", + "annotate('o = Full load torque',xy=(270,2100))\n", + "ylim(1000,2500)\n", + "xlim(250,500)\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "The plot is as shown:\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['Polygon', 'seterr', 'poly', 'cosh', 'ldexp', 'hypot', 'flatten', 'conjugate', 'diff', 'tan', 'Circle', 'roots', 'plot', 'isnan', 'eye', 'trace', 'fabs', 'floor', 'diag', 'invert', 'nan', 'modf', 'sqrt', 'frexp', 'source', 'add', 'degrees', 'take', 'var', 'zeros', 'prod', 'log10', 'plotting', 'product', 'exp', 'power', 'multinomial', 'copysign', 'transpose', 'expm1', 'ceil', 'test', 'beta', 'ones', 'isinf', 'sinh', 'vectorize', 'sign', 'trunc', 'cos', 'pi', 'e', 'f', 'tanh', 'det', 'radians', 'mod', 'binomial', 'solve', 'log', 'fmod', 'reshape', 'sin', 'log1p', 'gamma', 'interactive']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEZCAYAAAC5AHPcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "<matplotlib.figure.Figure at 0x3b84990>" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.4, Page number: 571" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Rf=109 #Field resistance(ohm)\n", + "Vf=300 #Rated field voltage(V)\n", + "n1=2000 #rpm\n", + "T_rated=285 #Rated torque(Nm)\n", + "n2=1975 #Dropped rpm\n", + "Kf=0.694 #Geometric constant(A.rad/sec\n", + "Ra=0.084 #Armature resistance(ohm)\n", + "\n", + "#Calculations:\n", + "If=Vf/Rf #Resulting field current(A)\n", + "wm1=2*pi*n1/60\n", + "w_ref=wm1\n", + "Vao=Kf*If*wm1\n", + "Ia=T_rated/(Kf*If)\n", + "wm2=2*pi*n2/60\n", + "Ea=Kf*If*wm2\n", + "Va=Ea+Ia*Ra\n", + "G=symbols('G')\n", + "x=solve(Vao-round(Va)+G*(w_ref-wm2),G)\n", + "\n", + "\n", + "#Results:\n", + "print \"Armature voltage,Vao:\",round(Va,0),\"V\"\n", + "print \"Multiplicative constant,G:\",float(round(x[0],2)),\"A.sec/rad\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Armature voltage,Vao: 408.0 V\n", + "Multiplicative constant,G: 3.04 A.sec/rad\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.5, Page number: 573" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "from sympy import *\n", + "\n", + "#Variable Declaration:\n", + "Km=0.22 #torque constant(V/(rad/sec))\n", + "Ra=1.03 #ohm\n", + "Pl=100 #Power load(W)\n", + "Va1=40 #Armature voltage(V)\n", + "Va2=50 # \" \" \"\n", + "\n", + "\n", + "#Calculations:\n", + "wm1=(Va1/(2*Km))*(1+sqrt(1-(4*Pl*Ra/Va1**2)))\n", + "wm2=(Va2/(2*Km))*(1+sqrt(1-(4*Pl*Ra/Va2**2)))\n", + "\n", + "#Results:\n", + "print \"for Va=40 V, wm=\",round(wm1,1),\"rad/sec\"\n", + "print \"for Va=50 V, wm=\",round(wm2,1),\"rad/sec\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "for Va=40 V, wm= 169.2 rad/sec\n", + "for Va=50 V, wm= 217.5 rad/sec\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.6, Page number: 575" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "\n", + "Rf=109 #Field resistance(ohm)\n", + "Vf=300 #Rated field voltage(V)\n", + "Ra=0.084 #Armature resistance(ohm)\n", + "Kf=0.694 #Geometric constant(A.rad/sec)\n", + "Tfl=285 #Full load torque(Nm)\n", + "nf=2500 #Speed at full load(r/min)\n", + "#wm=2500 #rated r/min\n", + "\n", + "#for part (1):\n", + "n1=2000 #r/min\n", + "n2=2500 #r/min\n", + "\n", + "\n", + "#Calculations:\n", + "#part (a):\n", + "If=Vf/Rf\n", + "w1=n1*2*pi/60\n", + "w2=n2*2*pi/60\n", + "Ea1=Kf*If*w1 #Avg Amature voltage(V)\n", + "Ea2=Kf*If*w2\n", + "Ia1=n1*Tfl/(nf*Kf*If)\n", + "Ia2=n2*Tfl/(nf*Kf*If)\n", + "Va1 = Ea1 + Ia1*Ra\n", + "Va2 = Ea2 + Ia2*Ra\n", + "Tl1=(n1/nf)*Tfl\n", + "Tl2=(n2/nf)*Tfl\n", + "\n", + "#part (b):\n", + "\n", + "# The dynamic equation governing the speed of the motor is\n", + "\n", + "# J*(dwm/dt)=Tmech-Tload\n", + "# wm=(pi/30)*n & wr=(pi/30)*nf\n", + "# Tload= (Tfl/wf)*wm\n", + "# Tmech = Kf*If*Ia=Kf*If*(Va-Ea)/Ra #Under armature-voltage control\n", + "\n", + "# Thus the governing differential equation is\n", + "# d(wm)/dt + 48.4*wm - 24.7*Va = 0\n", + " \n", + "# wm = wf + (wi-wf)*exp(-t/tau) #tau=1/48.4=20.7 msec\n", + "# n = 2500- 50*exp( -t/tau )\n", + "\n", + "# The armature current will decrease exponentially with the \n", + "# same 20.7 msec time constant from an initial value of \n", + "# (Vf - Vi)/Ra = 1190 A to its final value of 149 A.\n", + "\n", + "# Ia = 149 + 1041*exp(-t/tau)\n", + "\n", + "#part (c):\n", + "# J*d(wm)/dt = Tmech-Tload = Tf-(Tf/wm)*wm\n", + "# or d(wm)/dt + 1.18*wm - 310 = 0\n", + "\n", + "#In this case, the speed will rise exponentially to wm=wf=262 rad/sec as\n", + "# wm = 262-53*exp(-t/tau) #tau=1/1.18=845 msec\n", + "\n", + "#Results:\n", + "print \"part(a):\\n\"\n", + "print \"-------------------------------------------------\"\n", + "print \"r/min\\tw[rad/s]\\tVa(V)\\tIa(A)\\tTload[Nm]\"\n", + "print \"-------------------------------------------------\"\n", + "print n1,\"\\t\",round(w1),\"\\t\\t\",round(Va1),\"\\t\",round(Ia1),\"\\t\",Tl1,\"Nm\"\n", + "print n2,\"\\t\",round(w2),\"\\t\\t\",round(Va2),\"\\t\",round(Ia2),\"\\t\",Tl2,\"Nm\"\n", + "print \"-------------------------------------------------\"\n", + "print \"\\npart (b):\"\n", + "print \" The resultant motor speed, n = 2500 - 50*exp(-t/tau) where tau=20.7 msec\"\n", + "\n", + "print \"\\npart (c):\"\n", + "print \" The resultant motor speed, wm = 262 - 53*exp(-t/tau) where tau=845 msec\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "part(a):\n", + "\n", + "-------------------------------------------------\n", + "r/min\tw[rad/s]\tVa(V)\tIa(A)\tTload[Nm]\n", + "-------------------------------------------------\n", + "2000 \t209.0 \t\t410.0 \t119.0 \t228.0 Nm\n", + "2500 \t262.0 \t\t513.0 \t149.0 \t285.0 Nm\n", + "-------------------------------------------------\n", + "\n", + "part (b):\n", + " The resultant motor speed, n = 2500 - 50*exp(-t/tau) where tau=20.7 msec\n", + "\n", + "part (c):\n", + " The resultant motor speed, wm = 262 - 53*exp(-t/tau) where tau=845 msec\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.7, Page number: 581" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "\n", + "\n", + "#Variable Calculations:\n", + "f1=60 #Initial frequency(Hz)\n", + "f2=50 #Changed frequency(Hz)\n", + "Xs=0.836 #Saturated synch reactance(ohm)\n", + "Va=1+0j #Armature voltage(V p.u)\n", + "Ia=1+0j #Armature current(A p.u)\n", + "If_rated=2.84 #Rated field current(A)\n", + "p=6 #No. of poles\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "ns1=120*f1/p\n", + "ns2=120*f2/p\n", + "Eaf=Va-1j*Xs*Ia*exp(1j*0) #field voltage(V)\n", + "Ifo=abs(Eaf)*If_rated #motor field current(A)\n", + "\n", + "#for part(b):\n", + "#Eaf= (wm/wmo)*(If/Ifo)*Eafo\n", + "If=Ifo\n", + "\n", + "#Results:\n", + "print \"part(a):\"\n", + "print \"(i) The motor speed:\",ns1,\"r/min\"\n", + "print \"(ii) The motor field current:\",round(Ifo,2),\"A\"\n", + "print \"part(b):\"\n", + "print \"(i) The changed speed:\",ns2,\"A\"\n", + "print \"(ii) The mototr field current:\",round(If,2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "part(a):\n", + "(i) The motor speed: 1200.0 r/min\n", + "(ii) The motor field current: 3.7 A\n", + "part(b):\n", + "(i) The changed speed: 1000.0 A\n", + "(ii) The mototr field current: 3.7 A\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.8, Page number: 588" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "iF=2.84 #rated field current(A)\n", + "Vbase=220 #base voltage(V)\n", + "Eaf=220/sqrt(3) #Rms voltage line-to-neutral(V)\n", + "f=60 #Hz\n", + "p=6 #poles\n", + "P_rated=45*10**3 #rated power(W)\n", + "Xs_pu=0.836 #per unit synchronous reactance(ohm)\n", + "\n", + "#Calculations:\n", + "we=2*pi*f\n", + "Laf=sqrt(2)*Eaf/(we*iF) #Armature field reactance(H)\n", + "T_rated=P_rated/(we*2/p)\n", + "#setting rated values to reference values.\n", + "Tref=T_rated\n", + "iFref=iF\n", + "iQ=round((2/3)*(2/p)*Tref/(Laf*iFref),2)\n", + "iD=0\n", + "\n", + "#since theta_me=wc*t, iD=0,we have,\n", + "t=symbols('t')\n", + "wc=120*pi\n", + "def ia(t):\n", + " return iD*cos(wc*t)-iQ*sin(wc*t)\n", + "def ib(t):\n", + " return iD*cos(wc*t-2*pi/3)-iQ*sin(wc*t-2*pi/3)\n", + "def ic(t):\n", + " return iD*cos(wc*t+2*pi/3)-iQ*sin(wc*t+2*pi/3)\n", + "Ibase=P_rated/(sqrt(3)*Eaf)\n", + "Imax=round(ia((pi/(2*wc))))\n", + "Ia=1j*abs(round(Imax/sqrt(2)))\n", + "Eaf=1j*we*Laf*iF/sqrt(2)\n", + "Zbase=Vbase**2/P_rated\n", + "Xs=Xs_pu*Zbase\n", + "Va=1j*Xs*Ia+Eaf #line-to-neutral voltage\n", + "Vt=abs(sqrt(3)*Va)/Vbase #p.u terminal voltage(line-to-line)(V)\n", + "\n", + "#Results:\n", + "print \"part(a):\"\n", + "print \"\\tia(t)=\",ia(t),\"A\"\n", + "print \"\\tib(t)=\",ib(t),\"A\"\n", + "print \"\\tic(t)=\",ic(t),\"A\"\n", + "print \"part(b):\"\n", + "print \"\\tTerminal voltage:\",round(float(Vt),2),\"per unit\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "part(a):\n", + "\tia(t)= -167.01*sin(120*pi*t) A\n", + "\tib(t)= 167.01*sin(120*pi*t + pi/3) A\n", + "\tic(t)= -167.01*cos(120*pi*t + pi/6) A\n", + "part(b):\n", + "\tTerminal voltage: 1.3 per unit\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.9, Page number: 591" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "iF=2.84 #rated field current(A)\n", + "Vrated=220 #rated terminal voltage,l-l(V)\n", + "Ibase=118 #base current(A)\n", + "Eaf=220/sqrt(3) #Rms voltage, line-to-neutral(V)\n", + "f=60 #Hz\n", + "p=6 #poles\n", + "P_rated=45*10**3 #rated power(W)\n", + "Xs=0.899 #Synchronous reactance(ohm)\n", + "Xs_pu=0.836 #per unit synchronous reactance(ohm)\n", + "Tref=358 #Reference torque(Nm) (from Ex11.8)\n", + "\n", + "#Calculations:\n", + "Va=Vrated/sqrt(3) #base voltage, line to neutral(V)\n", + "we=2*pi*f\n", + "wm=(2/p)*we\n", + "Laf=sqrt(2)*Eaf/(we*iF) #Armature field reactance(H)\n", + "Ia=Tref*wm/(3*Va)\n", + "Ls=Xs/we #Synchronous inductance(mH)\n", + "delta=-atan(we*Ls*Ia/Va)\n", + "iQ_ref=sqrt(2)*Ia*cos(delta)\n", + "iD_ref=sqrt(2)*Ia*sin(delta)\n", + "iF_ref=(2./3)*(2/p)*Tref/(Laf*iQ_ref)\n", + "\n", + "#since motor is running at rated voltage, base voltage and rated voltage \n", + "# are assumed to be same.\n", + "Va_pu=Va/Va \n", + "Ia_pu=Ia/Ibase\n", + "\n", + "\n", + "#Results:\n", + "print \"The reqd motor field current:\",round(iF_ref,2),\"A\"\n", + "print \"Per unit voltage:\",Va_pu,\"p.u\"\n", + "print \"Per unit current:\",round(Ia_pu),\"p.u\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " The reqd motor field current: 3.7 A\n", + "Per unit voltage: 1.0 p.u\n", + "Per unit current: 1.0 p.u\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.10, Page number: 593" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "ns=4000 #rated speed(rpm)\n", + "Va=220 #rated voltage(V)\n", + "Ls=1.75*10**-3 #synchronous inductance(H)\n", + "Prated=25000 #Watts\n", + "n=3200 #rated OC speed(rpm)\n", + "p=2 #No. of poles\n", + "\n", + "#Calculations:\n", + "#for part(a):\n", + "Eaf=Va/sqrt(3)\n", + "wm=ns*pi/30 #rad/sec\n", + "Trated=Prated/wm\n", + "we=(p/2)*n*pi/30\n", + "lambdaPM=sqrt(2)*Eaf/we #flux linked wth permanent magnet(Wb) \n", + "Tref=Trated*0.65 #since motor is operated at 65% of Trated\n", + "iQref=(2./3)*(2/p)*(Tref/lambdaPM)\n", + "\n", + "#for part(b:)\n", + "lambdaD=lambdaPM #since iD=0\n", + "lambdaQ=Ls*iQref\n", + "lambdaa=sqrt((lambdaD**2+lambdaQ**2)/2) #rms line-to-neutral armature flux(Wb)\n", + "lambdaa_base=Eaf/wm\n", + "lambda_pu=lambdaa/lambdaa_base\n", + "\n", + "#for part(c)\n", + "lambdaD=sqrt(2*(lambdaa_base)**2-lambdaQ**2)\n", + "iDref=(lambdaD-lambdaPM)/Ls\n", + "Ia=sqrt((iDref**2+iQref**2)/2) #rms armature current(A)\n", + "Ibase=Prated/(sqrt(3)*Va)\n", + "I_pu=Ia/Ibase\n", + "\n", + "#Results:\n", + "print \"(a) Required quadrature-axis current:\",round(iQref,1),\"A\"\n", + "print \"(b) Resultant armature flux linkage\",round(lambda_pu,2),\"p.u\"\n", + "print \"(c) iD:\",round(iDref,1),\"A\"\n", + "print \" Rms value of armature current:\",round(Ia),\"A\"\n", + "print \" Per unit value of armature current:\",round(I_pu,2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Required quadrature-axis current: 48.2 A\n", + "(b) Resultant armature flux linkage 1.27 p.u\n", + "(c) iD: -66.1 A\n", + " Rms value of armature current: 58.0 A\n", + " Per unit value of armature current: 0.88 A\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.11, Page number: 600" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "V10=230/sqrt(3)\n", + "Nph=3\n", + "p=4\n", + "fe0=60\n", + "R1=0.095 #Armature resistance(ohm)\n", + "R2=0.2 #Rotor resistance(ohm)\n", + "X10=0.680 #Armature leakage reactance(ohm)\n", + "X20=0.672 #Rotor leakage reactance(ohm)\n", + "Xm0=18.7 #Inductice reactance(ohm)\n", + "\n", + "\n", + "#Calculations:\n", + "#taking two frequency values:\n", + "fe1=40\n", + "fe2=60\n", + "\n", + "for m in range(1,3,1):\n", + " if m==1:\n", + " fe=fe1\n", + " else:\n", + " fe=fe2\n", + " X1=X10*(fe/fe0)\n", + " X2=X20*(fe/fe0)\n", + " Xm=Xm0*(fe/fe0)\n", + " V1=V10*(fe/fe0)\n", + " \n", + " ws=4*pi*fe/p\n", + " ns=120*fe/p\n", + " V1eq=abs(V1*1j*Xm/(R1+1j*(X1+Xm)))\n", + " Z1eq=1j*Xm*(R1+1j*X1)/(R1+1j*(X1+Xm))\n", + " R1eq=Z1eq.real\n", + " X1eq=Z1eq.imag\n", + " \n", + "#Search over the slip until the Pload = Pmech \n", + " s=0 #slip initialised to 0\n", + " error=1\n", + " \n", + " while error >=0:\n", + " s=s+0.00001\n", + " rpm=ns*(1-s)\n", + " wm=ws*(1-s)\n", + " Tmech=(1/ws)*Nph*V1eq**2*(R2/s)\n", + " Tmech = Tmech/((R1+R2/s)**2 + (X1+X2)**2)\n", + " Pmech=Tmech*wm\n", + " Pload=10.5*10**3*(rpm/1800)**3\n", + " error=Pload-Pmech\n", + " \n", + " print \"\\nFor fe =\",fe,\"Hz :\"\n", + " print \"\\tTerminal voltage=\",round(V1*sqrt(3)),\"V l-l\"\n", + " print \"\\trpm =\",round(rpm)\n", + " print \"\\tslip =\",round(100*s,1),\"%\"\n", + " print \"\\tPload =\",round(Pload/1000,2),\"kW\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "For fe = 40 Hz :\n", + "\tTerminal voltage= 153.0 V l-l\n", + "\trpm = 1166.0\n", + "\tslip = 2.8 %\n", + "\tPload = 2.86 kW\n", + "\n", + "For fe = 60 Hz :\n", + "\tTerminal voltage= 230.0 V l-l\n", + "\trpm = 1721.0\n", + "\tslip = 4.4 %\n", + "\tPload = 9.17 kW\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.12, Page number: 608" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "V10=230/sqrt(3)\n", + "Nph=3\n", + "p=4\n", + "fe0=60\n", + "R1=0.095 #Armature resistance(ohm)\n", + "R2=0.2 #Rotor resistance(ohm)\n", + "X10=0.680 #Armature leakage reactance(ohm)\n", + "X20=0.672 #Rotor leakage reactance(ohm)\n", + "Xm0=18.7 #Inductice reactance(ohm)\n", + "n=1680 #rpm\n", + "Pmech=9.7*10**3 #Electromagnetic power(W)\n", + "\n", + "\n", + "#Calculations:\n", + "we0=2*pi*fe0\n", + "Lm=Xm0/we0\n", + "LS=Lm+X10/we0\n", + "LR=Lm+X20/we0\n", + "Ra=R1\n", + "RaR=R2\n", + "lambda_rated=sqrt(2)*V10/we0\n", + "lambdaDR=lambda_rated\n", + "#for specified operating condition\n", + "wm=n*(pi/30)\n", + "Tmech=Pmech/wm\n", + "iQ=(2/3)*(2/p)*(LR/Lm)*(Tmech/lambdaDR)\n", + "iD=lambdaDR/Lm\n", + "Ia=sqrt((iD**2+iQ**2)/2)\n", + "wme=(p/2)*wm\n", + "we=wme+(RaR/LR)*(iQ/iD)\n", + "fe=we/(2*pi)\n", + "Va=sqrt(((Ra*iD-we*(LS-Lm**2/LR)*iQ)**2 + (Ra*iQ+we*LS*iD)**2)/2)\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"Rms amplitude of the armature current:\",round(Ia,1),\"A\"\n", + "print \"The electrical frequency:\",round(fe,1),\"Hz\"\n", + "print \"Rms terminal voltage:\",round(sqrt(3)*Va,1),\"V line-line\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Rms amplitude of the armature current: 27.9 A\n", + "The electrical frequency: 58.4 Hz\n", + "Rms terminal voltage: 243.6 V line-line\n" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 11.13, Page number: 610" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "P_rated = 12*10**3 #Watts\n", + "V_rated = 230 #Rated line-line voltage(v)\n", + "Va_rated = 230/sqrt(3) #Rated line to neutral(V)\n", + "fe_rated = 60 #Hz\n", + "we_rated = 2*pi*fe_rated #rad/sec\n", + "lambda_rated = sqrt(2)*Va_rated/we_rated #Wb\n", + "I_rated = P_rated/(sqrt(3)*V_rated) #A\n", + "Ipeak_base = sqrt(2)*I_rated #A\n", + "p = 4 #poles\n", + "\n", + "V10=V_rated/sqrt(3)\n", + "R1=0.095 #Armature resistance(ohm)\n", + "R2=0.2 #Rotor resistance(ohm)\n", + "X10=0.680 #Armature leakage reactance(ohm)\n", + "X20=0.672 #Rotor leakage reactance(ohm)\n", + "Xm0=18.7 #Inductice reactance(ohm)\n", + "\n", + "#Calculations:\n", + "Lm = Xm0/we_rated;\n", + "LS = Lm + X10/we_rated;\n", + "LR = Lm + X20/we_rated;\n", + "Ra = R1\n", + "RaR = R2\n", + "#operating point:\n", + "n = 1680 #rpm\n", + "lambdaDR=lambda_rated\n", + "wm = n*pi/30\n", + "wme = (p/2)*wm\n", + "Pmech = 9.7*10**3\n", + "Tmech = Pmech/wm\n", + "lambda_DRpu=[0]*42\n", + "iDpu=[0]*42\n", + "Iapu=[0]*42\n", + "fe=[0]*42\n", + "Vapu=[0]*42\n", + "\n", + "for n in range(1,43,1):\n", + " lambdaDR = (0.8+(n-1)*0.4/40)*lambda_rated\n", + " lambda_DRpu[n-1]=lambdaDR/lambda_rated\n", + " iQ=(2/3)*(2/p)*(LR/Lm)*(Tmech/lambdaDR)\n", + " iD=(lambdaDR/Lm)\n", + " iDpu[n-1]=iD/Ipeak_base\n", + " iQR=-(Lm/LR)**iQ\n", + " Ia=sqrt((iD**2+iQ**2)/2)\n", + " Iapu[n-1]=Ia/I_rated\n", + " we=wme-(RaR/LR)*(iQ/iD)\n", + " fe[n-1]=we/(2*pi)\n", + " Va_rms=sqrt(((Ra*iD-we*(LS-Lm**2/LR)*iQ)**2 +(Ra*iQ+ we*LS*iD)**2)/2)\n", + " Vapu[n-1]=Va_rms/Va_rated\n", + "\n", + "#Results:\n", + "print \"The required plot is as shown:\"\n", + "plot(iDpu,Iapu)\n", + "plot(iDpu,Vapu,':')\n", + "xlabel('i_D [per unit] ')\n", + "ylabel('per unit')\n", + "annotate('Ia',xy=(0.21,1.05))\n", + "annotate('Va',xy=(0.21,0.85))\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "The required plot is as shown:\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['vectorize', 'prod', 'plotting', 'Circle', 'diag', 'sinh', 'trunc', 'plot', 'eye', 'f', 'det', 'tan', 'product', 'gamma', 'roots', 'radians', 'sin', 'fmod', 'expm1', 'ldexp', 'zeros', 'cosh', 'info', 'interactive', 'conjugate', 'linalg', 'take', 'trace', 'beta', 'exp', 'random', 'frexp', 'fft', 'ceil', 'ones', 'copysign', 'isnan', 'multinomial', 'cos', 'transpose', 'solve', 'diff', 'invert', 'degrees', 'pi', 'tanh', 'Polygon', 'fabs', 'reshape', 'sqrt', 'floor', 'source', 'add', 'poly', 'mod', 'sign', 'hypot', 'power', 'binomial', 'log', 'var', 'log10', 'e', 'seterr', 'log1p', 'flatten', 'nan', 'modf', 'isinf', 'test']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEQCAYAAABIqvhxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtclGX6+PHPAAoqmlpACiSGB2Q4h24eMExJ8ZzaEqa2\nHsq0vq19Xfut25ZYra6pleV+zTI72vmgmUq7HcZTHlBQUdM8IkOiiAcEBIG5f388ORspwsgMzzBc\n79eL187Mc5jr3vC5uJ77ue/boJRSCCGEEDZw0zsAIYQQ9Y8kDyGEEDaT5CGEEMJmkjyEEELYTJKH\nEEIIm0nyEEIIYTOHJo8JEybg5+dHeHj4Nbe/9957REREEB4eTmxsLDt37rRuS01NJTw8nNDQUObN\nm+fIMIUQQtjIoclj/PjxpKamVrm9c+fObN68mczMTJ5//nkmTZoEQGlpKVOmTCE1NZU9e/bw2Wef\nkZGR4chQhRBC2MChySMuLo5WrVpVub1bt240b94cgJ49e5KTkwPAtm3bMBqN+Pv74+HhQVJSEmvW\nrHFkqEIIIWzgNH0eS5cuZdiwYQCYzWYCAwOt2wICAjCbzXqFJoQQ4nc89A4AwGQysXz5cjZv3gyA\nwWDQOSIhhBDXo3vy2LNnD5MmTSI1NdV6iysgIIDs7GzrPtnZ2ZUqkSs6dOjAkSNH6ixWIYRwBcHB\nwRw+fLh2J1EOduzYMRUWFnbNbVlZWSo4OFht2bKl0ueXLl1S7dq1U2azWV2+fFnFxsaqnTt3XnV8\nHYSvq1mzZukdgkNJ++ovV26bUq7fPntcOx1aeSQnJ7N+/XrOnDlDYGAgs2fPpqysDIDJkyfz7LPP\ncu7cOaZMmQJAo0aN2L59O15eXixZsoT+/ftjsVgYO3YsMTExjgxVCCGEDRyaPD788MPrbl+2bBnL\nli275rbExEQSExMdEZYQQohacpqnrcTV4uPj9Q7BoaR99Zcrtw1cv332YPj1/le9ZDAYqMfhCyGE\nLuxx7ZTKQwghhM0keQghhLCZJA8hhBA2k+QhhBDCZpI8hBBC2EyShxBCCJtJ8hBCCGEzSR5CCCFs\nJslDCCHsoMJSQVlFmd5h1BlJHkIIYQfP/PAMb+16S+8w6oxMTyKEEHZwsfQizRo3w83g/H+Ty/Qk\nQgihE6UUj619DHOBtkR2c8/m9SJx2EvDaakQQtiRwWBgcKfBtG7SWu9QdCG3rYQQoobKKsr4z9H/\nMLDjQL1DqRWnv201YcIE/Pz8CA8Pv+b2AwcO0L17d7y8vFi4cGGlbUFBQURERBAdHU23bt0cGaYQ\nQtTIxcsX+Wz/Z5RbyvUORXcOrTw2btyIt7c348aNIzMz86rteXl5ZGVlsXLlSlq1asX06dOt29q3\nb8/OnTtp3brqklAqDyGEoymluHj5Ii08W+gdit04feURFxdHq1atqtzu4+NDbGwsjRo1uuZ2SQxC\nCL2tPLCS6d9Mr37HBsZpO8wNBgMJCQlERESwePFivcMRQjRQw0KGsXigXIN+z0PvAKqydetWfH19\nycvLY8CAAYSEhNCvX7+r9ktJSbG+jo+Pl7WHhRC1NnfjXCJvjWRgx4G4Gdzw9PDUO6RaMZlMmEwm\nu57TaZOHr68voN3aGjVqFGlpadUmDyGEsIdBnQYR0CJA7zDs5vd/WM+ePbvW53SK21a/79soLi6m\nuLgYgKKiIlJTUzEajXqEJoRoACzKwnu737POTRXhF9Fgx2/UlEMrj+TkZNavX8+ZM2cIDAxk9uzZ\nlJVp/3EmT55Mbm4uXbt2paCgADc3NxYtWsT+/fs5ffo09957LwaDgeLiYu6//36GDh3qyFCFEA2Y\nAQP78vZxruQcvs189Q6nXpBBgkKIBiu3MJdbvW/VO4w65/SP6gohhLM6eu4oIz8ZKX+A3iCpPKqw\nZQt89RXMmQMGg0O+QgihswpLBe5u7nqHUeek8nCgkBD4z39g+nSov+lVCPFbn+77lNmm/z5p1BAT\nh71I5XEd585BQgL07g0LF0oFIkR9l1+cT2lFKW2bt9U7FF1J5eFgrVpp1ceGDVKBCFFfvb/nfbLO\nZwFwc9ObG3zisBdJHtWQBCJE/WZRFi6VX9I7DJcjt61qSG5hCVF//Jz/M51u7qR3GE5LblvVIalA\nhKgfSstLmbBqAudLzusdikuTysNG587BPfdA9+6waJFUIEI4C4uyWNcQV0phkH+cVZLKQwdXKpC0\nNJgyBSwWvSMSQuzK3cWwj4ZZ30vicDypPG7QxYswaBAEB8OyZeAuj4sLoRulFDkXc1xqJlxHsse1\nU5JHLRQVwdChcOut8M474OG0E9wL4XrWHlqLRVkY3Gmw3qHUO3LbSmfNmsHXX0N+PoweDb9OGCyE\nqAO3NL1FZsDVkVQedlBSAvfdp926+vhj8Kzfi44J4bTSctKIvDWSxu6N9Q6lXpPKw0l4ecHnn2vJ\n49574ZKMRxLCId5If4ODZw7qHYZAKg+7KiuD8ePBbIbVq6F5c70jEqL+Ky0vrfdriDsbp688JkyY\ngJ+fH+Hh4dfcfuDAAbp3746XlxcLFy6stC01NZXw8HBCQ0OZN2+eI8O0m0aN4N13tRl5+/WDs2f1\njkiI+u1CyQViXo+htLxU71DE7zi08ti4cSPe3t6MGzeOzMzMq7bn5eWRlZXFypUradWqFdOnTweg\ntLSUkJAQNm3ahJ+fH927d+f1118nOjq6cvBOVnlcoRTMmAH//rc2JsTPT++IhKi/LpRc4Cavm/QO\nw6U4feURFxdHq1atqtzu4+NDbGwsjRo1qvT5tm3bMBqN+Pv74+HhQVJSEmvWrHFkqHZlMMD8+TBq\nFMTFwYkTekckRP2RlpPGs+uftb6XxOGcnLLD3Gw2ExgYaH0fEBCA2WzWMSLbGQzwzDPaKPTeveHw\nYb0jEqJ+6NC6A32C+ugdhqiGUw5rs2VqgZSUFOvr+Ph44uPj7R9QLTzxhNZxftddkJoKVXT/CNGg\n7fhlB62btOb2VrfTqkkr4trF6R2SSzGZTJhMJrue0ymTR0BAANnZ2db32dnZlSqR3/pt8nBWkyZp\nCaRfP1i5UptUUQjxX+kn02nfsj23t7pd71Bc0u//sJ49e3bVO9eQU9y2+n3HTdeuXdm7dy85OTmU\nlZXxySefkJiYqFN09pGUpE1hMmyYVoEI0dDlF+dbXz98x8MkBCfoGI2wlUOftkpOTmb9+vWcOXMG\nPz8/Zs+eTdmvc3hMnjyZ3NxcunbtSkFBAW5ubjRv3pz9+/fj7e3NunXrmDFjBhaLhbFjxzJz5syr\ng3fSp62u58cftYGEL78Mycl6RyOEPpRS3PnmnXw86mOCWgbpHU6DIxMj1sPkAZCZCYmJ8Le/wdSp\nekcjhD7KKspo5N6o+h2F3Tn9o7ri2sLDYeNGePFFePZZWZVQNAzZF7IZ9tEwLEpbBEcSR/0mlYeO\ncnNhwADtUd6XXwY3SeXChSml2JW7i+g20dXvLBxKblvV8+QBcP681onepo3WoS4z8gpXsjt3N79c\n/IXEjvX7gRdXI7etXEDLlvDNN9qkigMHQkGB3hEJYT+XKy5TeLlQ7zCEA0jl4SQqKuDxx2HzZli3\nTqtEhKiPss5n4efth5eHl96hiCpI5eFC3N1h8WJtUakePeCgLFkg6qk5G+ew1bxV7zCEg0nl4YSW\nL4enntJGo//hD3pHI0T1LMqCm0H7W1QpZdMUQ6LuSeXhoiZMgGXLYMgQWLtW72iEuL7S8lJiX4/l\nQskFwLa56UT9JZWHE9u6VRuNPns2PPyw3tEIUbWcghz8W/jrHYaoIak8XNydd2qDCefP10ajWyx6\nRySE5uf8n5m7ca71vSSOhkeSh5Pr0AG2bAGTCcaOhVJZjVM4Ab9mfnS8uaPeYQgdyW2reuLSJXjg\nAW1d9C+/hOss0CiEQxw9dxSlFMGtg/UORdSS3LZqQJo0gU8/heho6NkTsrL0jkg0NN8d/Y4dv+zQ\nOwzhJKTyqIdeflnrB1m1CmJj7Xdeb29vCgtlNLD4r4ulF2nu2VzvMISdSeXRQE2bpg0oTEzUxoLY\nizxiKX5v8IeD2Xd6n95hCCcklUc9tmMHDB+uJZPp06G21/7mzZtz8eJFCgsLGTJkCAUFBRQVFfHc\nc89x33332SdoUa9cKrtEk0ZN9A5D2JnTz6o7YcIE1qxZg6+vL5mZmdfc5/HHH+e7777D09OTN998\nk+hobbrmoKAgWrRogbu7O40aNWL79u1XB9/AkwdAdjYMHqw91rt4MTSqxRIJV5JHRUUFpaWlNG3a\nlDNnztC1a1eOHj0qlUkDkFeUx5Q1U/ho1Ed4uHnoHY5wEKe/bTV+/HhSr7Ng9+eff86JEyfYt28f\nb775JuPHj7duMxgMmEwmMjIyrpk4hCYwEDZtArNZm5X3/Pnan7OsrIxp06YRFhZGQkICp0+f5uTJ\nk7U/sXB6tzS9hWl3TpPEIarl0OQRFxdHq+s8U7p27VrGjh0LQHR0NOXl5eTk5Fi3N/SqoqaaN9c6\nz7t00Z7EOnasdud79913KSgoIDMzk4yMDHx9fSkvL7dPsMLp/Jz/M+sOrQO0P9p63dZL54hEfaBr\nh7nZbCYwMND6PiAgALPZDGi/xAkJCURERLB48WK9Qqw3PDzglVdgyhRtVt5Nm278XCUlJfj6+mIw\nGNiwYQNZ8lywSyu6XMTJQqkshW10r02rqi62bNmCn58feXl5DBgwgJCQEPr163fVfikpKdbX8fHx\nxMfHOyjS+uGxx6BjRxgxAl54Af70p5ofe6VP44EHHqB///5ERkYSGxtLly5dHBOs0M3Jiye5yesm\nmjZqSnSbaFka1sWZTCZMJpNdz+nwp62OHz/OkCFDrtlhPnHiRBITExk1ahQAYWFhfPPNN/j7V54n\nZ+5cbQ6dmTNnVvpcOsyrduCANivvsGEwb562XogQVzy65lEGdRrEwI4D9Q5F6MDpO8yrM3DgQFas\nWAFAeno67u7u+Pv7U1xcTHFxMQBFRUWkpqZiNBr1DLXeCQmBbdsgI0NLILK8rbCo/86suXjgYkkc\nolYcmjySk5Pp0aMHBw8eJDAwkOXLl7N06VKWLl0KwMiRI/H398doNDJp0iTeeustAHJzc+nevTtR\nUVFER0dz1113MXToUEeG6pJat4bUVO2JrB494OhRvSMSeimrKKPbG904U3wGkAGhovZkkGAD8a9/\nwXPPwUcfQQPvFmqwsi9kE3hTYPU7CpdX729bibrz6KPw/vtw//1aIpGc6/qOnD3C8xuet76XxCHs\nSZJHA9KvH2zeDK+9Bg89JGuDuDrfZr50bC1rbgjHkNtWDVBhITz4IJw8CZ9/Dm3a6B2RsJej545S\nbimn082d9A5FODG5bSVuiLe3tjZIYiJ06wYy+4vr2JC1QdbcEHVCKo8GbtUq7RbWggUwbpze0Ygb\ncaHkAjd53aR3GKIekcpD1NqwYdr66M8/D48/DmVlekckbHXvx/eSeeras1YL4ShSeQhAm4133Dg4\nd067pXXrrXpHJGqqtLwUTw9PvcMQ9YhUHsJuWrbUViW85x5tadstW/SOSFTldNFpBn0wiHKLNtOx\nJA6hB6k8xFXWroXx4yElBR55pPYrFAr7Ukqx8+ROYtvacQF70aA4/UqCjibJw3EOH9Zm5r3jDvi/\n/4MmshKprvae3svhs4cZHjJc71CEC5DbVsJhOnTQbl2VlmoLTMm8WPpSSnGp7JLeYQhhJZWHuC6l\ntLXRn38eli3TpnkXdePI2SP4efvh3dhb71CEi5HKQzicwQD/8z/aeJBHH4WZM0FWpK0br25/le05\nMoJTOCepPESN5eXBAw9oyePDD8HPT++IXM/liss0dm+sdxjCxdVJ5dG3b98afSZcn48PrFsHcXFa\nR3pt1kkXVystLyVmaQznS87rHYoQ1aoyeVy6dIn8/Hzy8vI4e/as9Sc7O5usrKwanXzChAn4+fkR\nHh5e5T6PP/44RqORmJgYMjIyrJ+npqYSHh5OaGgo8+bNs6FJwpHc3WH2bHjjDRg1Slsn3WKp/jhR\nPU8PTzaM30BLr5Z6hyJE9VQVXnrpJRUUFKQaN26sgoKCrD9dunRRCxYsqOqwSjZs2KDS09NVWFjY\nNbd/9tlnatiwYUoppdLT01VkZKRSSqmSkhIVFBSkzGazKisrU7GxsSo9Pf2q468TvqgDWVlKde+u\n1KBBSp05o3c09dOe3D1qxr9n6B2GaGDsce2ssvKYNm0ax44dY8GCBRw7dsz6s3//fqZPn16jxBQX\nF0erVq2q3L527VrGjh0LQHR0NOXl5ZjNZrZt24bRaMTf3x8PDw+SkpJYs2aNTUlRON5tt8H69RAa\nCjExMir9Rtze6nYSOyTqHYYQNvOoasP333/P3XffTdu2bfniiy+u2j5ixIhaf7nZbCYw8L+rmwUE\nBGA2m8nJybnqc5PJVOvvE/bXqJF26youDoYPhxkz4H//F9zkOb4qbTVvxbuxN2G+YTRr3Iw+7fvo\nHZIQNqsyeaxfv567776b1atXY7jG/BT2SB6APC3lIoYM0dYFSUrSqpG334abb9Y7Kud0/PxxWjdp\nTZhvmN6hCHHDqkwes2fPBuDtt9922JcHBASQnZ3NH/7wB+C/lUhZWRnZ2dnW/bKzsytVIr+VkpJi\nfR0fH098fLzD4hXX164dbNigjQWJidHWTI+L0zsq53D8/HHa3dQOg8HA/WH36x2OaGBMJpPd795U\nO86jqKiITz/9lOzsbCy/PlZjMBh45plnavQFx48fZ8iQIWRmXr3ewOeff87777/Pl19+SXp6OuPH\nj2f37t2UlJQQEhLC5s2b8fX1pUePHixdupSYmJjKwcs4D6e1Zg1MnKgNLPzb37SntBoqpRR93+3L\nsqHLuL3V7XqHI4Rdrp1VVh5XDBo0CD8/P+644w7cbbwCJCcns379es6cOUNgYCCzZ8+m7NfVhiZP\nnszIkSP54YcfMBqNeHp68tZbbwHg5eXFkiVL6N+/PxaLhbFjx16VOIRzGzQIdu6EMWPg+++1KsTf\nX++o6pZFWXAzuGEwGPhu3HfXvP0rRH1VbeURFhbG3r176yoem0jl4fwqKmDuXG1+rGXLYPBgvSOq\nG8fPH2fMF2PYOH6jJA3hdOpkhHmvXr2cNnkI5+fuDn//O3z2mXYLa9o0baZeVxfUMoiPRn0kiUO4\nrGorjy5dunD48GHat2+Pp6e2YpnBYGDPnj11EuD1SOVRv5w9C5MmwbFj8MEH0KWL3hHZ1zeHv+FU\n0SnGRY7TOxQhrqtO+jzWrVtXqy8Q4orWreHzz7WpTeLi4LnnXGulwnYt29G6SWu9wxCiTlRbeZw4\nceKan992220OCcgWUnnUXwcOaDP0BgTAm2/CLbfoHdGN+ebwN3QP7E4LzxZ6hyJEjdVJ5TFw4EDr\nfduSkhKOHTtG586d2bdvX62+WDRsISHadCZ//ztERmqDChMS9I7Kdj9m/4h/C38Z8CcaHJvX89i1\naxeLFy9m2bJljoqpxqTycA3ffQcPPqiNTv/HP8DLS++Iru/kxZO0ad5G7zCEuGG6rCQYFRXF1q1b\na/WlQvxW376wezdkZUG3buAEz2JU6XzJeQZ+MJDS8gbwyJgQ11Ft5bFw4ULra4vFQnp6OidPnnSK\niQql8nAtSsG778Jf/gJPPqlNsOgsI9OVUtbbtxWWCtzdnCQwIW5AnVQeFy9epLCwkMLCQkpKSrjn\nnntkenThEAaDdvsqLQ1Wr4a779aqEb2tO7SOqWumWt9L4hBC1jAXTqqiAhYuhPnztf8dO1a/R3ov\nlV3ifMl56ecQLsMe105JHsKp7dqlzY/VuTO89pq2jnpdWLx9MT0CexDTRuZUE65Hlw5zIepSVBTs\n2AEdOkBEBHz5Zd18b+ebO3NzE1mQRIiqXDd5WCwWXn755bqKRYhr8vKCefO0+bGefFK7hXXunH2/\nw6IsrDqwyvrXWEJwAu1atrPvlwjhQq6bPNzc3Pj444/rKhYhrqtnT+02VsuWWhXyzTf2O3e5pZzV\nP6+moLTAficVwoVV2+fxxBNPYLFYGDVqFM2aNbN+7gzra0ifR8P13XcwYQIMGAALFkDz5rafQynF\nqaJT3Op9q/0DFMKJ1UmHeXx8/DWnlf7hhx9q9cX2IMmjYbtwAaZPh2+/1SZbtHV6k00nNrFo2yI+\nve9TxwQohJNy+qetUlNTmTFjBhUVFTz44IP8v//3/yptz8/PZ8yYMZw4cQJvb2+WL1+O0WgEICgo\niBYtWuDu7k6jRo3Yvn371cFL8hBot68efhj699ce7b3ppqr3vfL7IgP+RENWJ09b5eTkMGbMGBJ+\n/bPu4MGDvP7669WeuLS0lClTppCamsqePXv47LPPyMjIqLRPSkoKPXr0YN++fbz77rs89NBD1m0G\ngwGTyURGRsY1E4cQV/TvD5mZ2jiQ8HBITa163xRTCu/uftf6XhKHEDem2uQxZswYhgwZwqlTpwAI\nDg7mlVdeqfbE27Ztw2g04u/vj4eHB0lJSVeNTD948CB9+vQBoHPnzpw+fZqTJ09at0tVIWqqRQtY\nulSb3v2RR2DiRDh//ur9Hol9hPvD7q/7AIVwMdUmj/z8fJKSknD/dZIhDw8PPDyqnckds9lMYGCg\n9X1AQABms7nSPuHh4XzxxRcAbN++naysLOv6IQaDgYSEBCIiIli8eHHNWyQatIQErQpp3BjCwuDL\nVRU8vu5xzpdomaRN8zZ4enjqHKUQ9V+1WaBZs2bk5+db32dkZFiXo72emqzdPGvWLKZMmYLRaKRL\nly7ExsZaj9uyZQt+fn7k5eUxYMAAQkJC6NevX7XnFKJ5c1iyBO6/HyZNcsc3vjfnIxrT0l/vyIRw\nHdUmjxdffJF77rmHo0eP0rt3b06cOMGnn1b/dEpAQADZ2dnW99nZ2ZUqEYAWLVqwYsUK6/vg4GA6\ndeoEgJ+fHwA+Pj6MGjWKtLS0ayaPlJQU6+v4+Hji4+OrjU24tgslF9icvZmBdw1kzx5ISRnFH2K0\nR3rHjHGdZW+FqCmTyWT/mdBVDVy+fFnt2LFDpaWlqdLS0pocoi5duqTatWunzGazunz5soqNjVU7\nd+6stM+FCxdUWVmZUkqp9957T40aNUoppVRRUZEqKipSSilVWFioevfurVatWnXVd9QwfNHAHD93\nXM3494xKn+3YoVRkpFIDBiiVlaVTYEI4CXtcO6utPIqLi1m0aBGbNm3CYDDQq1cv/vznP9OkSZPr\nHufl5cWSJUvo378/FouFsWPHEhMTw9KlSwGYPHkye/fuZfz48Xh5edGxY0fefPNNAE6dOsXw4cMx\nGAwUFxdz//33M3To0FonSuG6LpZepMxSRusmrWnXsh0vJLxQafsdd2hTvc+fr71++ml49FHnWS9E\niPqm2nEegwcPpm3btiQnJ6OU4uOPPyYnJ4evv/66rmKskozzEFf8Y8M/uKXpLUyOnVztvgcPauNC\nLl3SBhdGRtZBgEI4kToZJBgWFsbevXur/UwPkjwatt8O8FO/WemvJiwWeOstmDlTm+bkmWegaVNH\nRSqEc6mTQYIxMTGVBumlpaU5xbxWomFTSnHX23dx9NxRoGZP9/2Wm5s2FiQzE06c0AYX/vvfjohU\nCNdUbeUREhLCzz//TGBgIAaDgRMnTtC5c2c8PDwwGAzs2bOnrmK9ilQeDVtuYa7dJjVctw6mTNFm\n7n3xRfj1YT8hXFKd3LY6fvz4dU8QFBRUqwBqQ5JHw5J1PosXNr/A4oGLba40aqKoCJ59FpYvh+ee\n0/pF3GS5NOGCnH5iREeT5NGwlFWU8e3Rb0nsmOjQ78nM1KqQ8nJt6duoKId+nRB1TpKHJA+Xt+OX\nHVyuuEyPwB51+r1XOtT/9jcYPVqrSG5kzRAhnJGsYS5c3pniM5y9dLbOv/dKh/revdoEi6Gh8Omn\nIH+rCKGRykM4nb2n99Llli5ONV36xo0wdSrceiu8+iqEhOgdkRA3TioP4ZJmr5/NobOH9A6jkrg4\nSE+HQYOgVy9tfEhRkd5RCaEfqTyEUyguK6Zpo/oxSu/kSZgxAzZsgJdeghEjZLJFUb9I5SFcwokL\nJ4h7Kw6LsugdSo20aQPvvw/vvQezZsGAAXDggN5RCVG3pPIQTqHwciHejb31DsNmZWWweDH84x/w\npz9p05y0aKF3VEJcn1Qeot5asWcF8zfPt76vj4kDoFEjeOIJ2LcPzp7VOtLfeUd71FcIVyaVh9DF\nyYvaWvVtmrfRORL72r4dHntMm+r91VchNlbviIS4mlQeol6Zs3EOuYW5gJY0XC1xAHTrBlu3alOb\nDBmijRXJzdU7KiHsT5KHqDNBLYNwM7j+r5ybG4wfr3Wit24NYWEwbx6UluodmRD249B/yampqYSH\nhxMaGsq8efOu2p6fn09iYiJGo5E//OEP7Nu3r8bHCudXUl7CukPrrO9Hh4/Gt5mvjhHVrZtu0lYu\n3LJF+wkNhS+/lFHqwkXUeiHbKpSUlKigoCBlNptVWVmZio2NVenp6ZX2eeyxx9Szzz6rlFLqwIED\nqnv37jU+9te+GkeFL+zgdOFpNWnVJFVhqdA7FKfw7bdKhYUp1aePUrt26R2NaMjsce10WOWxbds2\njEYj/v7+eHh4kJSUxJo1ayrtc/DgQfr06QNA586dOX36NL/88kuNjhXOqaS8hNNFpwHwaebDG0Pf\naBC3qmqib1/IyIA//hH694dJk7QBh0LURw77V202mwkMDLS+DwgIwGw2V9onPDycL774AoDt27eT\nlZXFiRMnyMnJqfZY4ZyWZyznzfQ39Q7DaXl4wCOPVO4Pee45KC7WOzIhbOPhqBPXZLGeWbNmMWXK\nFIxGI126dCE2NtbmRX5SUlKsr+Pj44mPj7cxUlFb5ZZyPNy0X6VHYh+RSqMGWraEF17Q1g2ZORM6\nd9YGGo4ZIwtQCfszmUyYTCa7ntNhySMgIIDs7Gzr++zs7ErVBECLFi1YsWKF9X1wcDCdO3fm8uXL\n1R57xW+Th9DHgPcH8FL/lwj3C5fEYaP27eGjj7QO9f/9X1i0CBYsgF/v5gphF7//w3r27Nm1PqfD\n/qV37dphAOXxAAAX8UlEQVSVvXv3kpOTQ1lZGZ988gmJiZVXgCsoKKC8vByA999/n5iYGFq2bFmj\nY4Xz+HDkh4T7hesdRr3WvTv8+CM8+aQ2NmTQIG0tESGclcOSh5eXF0uWLKF///5ERkYyYsQIYmJi\nWLp0KUuXLgVg7969GI1GIiMjWblyJcuWLbvuscI5mAvMjP58tHUiQ59mPjpH5BoMBkhKgp9+gnvu\n0TrYJ06EnBy9IxPiajI9ibCZRVnYfGIzce3i9A7FpV24oA0uXLpU62R/8klt7IgQtSXTk4g6Yzpu\nIvVwKgBuBjdJHHXgpptgzhzYvVt7pLdTJ3j5ZRmpLpyDJA9RI57unni6e+odRoMUEADLl8O338L3\n32tPZr3zDlRU6B2ZaMjktpWo0jeHv6FP+z40dm+sdyjiNzZtgr/+Fc6f1yqTIUNkJUNhG7ltJRxG\nKcXXP39N9oXs6ncWdapXL9i4Ef75T3jqKW199Y0b9Y5KNDRSeQgrpRQnLpygXct2eociaqiiAlas\n0JbDDQmB55+HO+7QOyrh7KTyEHZ15NwRxq8aLwm5HnF3h3Hj4OBB7fbV0KEwahTs3693ZMLVSeXR\nwCmlKLOUWfs1lFI2TxEjnEdxMfzrX9pU8ImJWkVy++16RyWcjVQeotZe2PwCi7Yusr6XxFG/NW0K\nM2bA4cPa1CfdumljRLKl60rYmVQeDdzF0os0adTEOrGhcC35+dpcWa+/DsnJ2iSM/v56RyX0JpWH\nsJlSimEfDbM+RdXcs7kkDhd2880wd6425YmXF4SHw7Rpsq66qD1JHg2MwWBg1l2z8G8hf342JL6+\nWgVyZaXn0FD4y1/g1Cl94xL1lySPBuDouaPM3TjX+j6mTYxMnd5AtWmjTXGSmalNc9KlC0yfLpWI\nsJ1cQRqAW5reQlDLIL3DEE7E3x9efVVLImVlWiXyxBOyLK6oOUkeLmrTiU38lPcTAC08W5Acnlzt\nMXfffTf//ve/K3328ssvM3XqVIfEKPTn7w+vvKKtHaIUGI3w5z/DL7/oHZlwdpI8XFTW+SxyC227\nF5GcnMxHH31U6bOPP/6Y0aNH2zM04YTattVuZ+3bpw08DAuDqVPh+HG9IxPOSh7VdSE7ftlBbNvY\nGz7+7NmzdOnShZycHDw8PDh+/Dh33XUXiYmJ7Ny5kwsXLjBixAj++c9/2jFq4YxOn4aXXtIe8R02\nTHvEt2NHvaMS9uL0j+qmpqYSHh5OaGgo8+bNu2p7bm4uffv2xWg00rlzZ+sKgwBBQUFEREQQHR1N\nt27dHBmmSyirKGOWaRb5xfk3fI7WrVvTrVs31q5dC8BHH31EUlIS8+fPJy0tjZ9++olt27axc+dO\ne4UtnJSvr/aI7+HDEBQEPXrA6NGyNK74DeUgJSUlKigoSJnNZlVWVqZiY2NVenp6pX2eeuop9de/\n/lUppVReXp5q2bKlKikpUUopFRQUpPLz86/7HQ4Mv16wWCwqryjPrudcsWKFSk5OVkopFRUVpdLT\n09WLL76oIiIiVGRkpPLx8VErVqyw63cK51dQoNS8eUr5+Sk1bJhSW7fqHZGoDXtcOx1WeWzbtg2j\n0Yi/vz8eHh4kJSWxZs2aSvsEBgZSUFAAQEFBAT4+Pnh6/nfBISW3pK7r+2Pf8/i6x+16zqFDh/Ld\nd9+RkZFBcXExzZo141//+hebN29m165dDBo0iLKyMrt+p3B+zZtry+AeOwYJCdpa63ffDf/5j9bR\nLhoehyUPs9lMYGCg9X1AQABms7nSPg899BD79u2jbdu2REZGsmhR5TmWEhISiIiIYPHixY4Ks96p\nsFRYk+rd7e/m3Xvftev5vb296dOnD+PHj2f06NGUlJTg7e1Ns2bNOHPmDOvWrZP5rxqwJk3g0Ufh\n0CH405+0J7O6dYMvvgCLRe/oRF1y2LwUNbnAzJkzh6ioKEwmE0eOHCEhIYHdu3fTvHlztm7diq+v\nL3l5eQwYMICQkBD69et31TlSUlKsr+Pj44mPj7djK5zPlDVTGNxpMEM7D8VgMOBhsP9/wuTkZEaM\nGMEnn3xCp06dCA8Pp2PHjgQHB9OrVy+7f5+ofxo10qaCHzMGvvpK6x/529+0SRnHjAFPWbHYqZhM\nJkwmk13P6bCnrTZu3Mi8efP4+uuvAZg/fz6XL1/mqaeesu4zYMAAnn76aXr27AlA3759mTt37lUd\n5HPnaqOjZ86cWTn4Bvi0VV5RHjc3vVlGiAunohSYTPDCC7Bnj1aRTJ4MN92kd2TiWpz6aauuXbuy\nd+9ecnJyKCsr45NPPiExMbHSPsHBwXz77bcAnDp1iv379xMUFERxcTHFxcUAFBUVkZqaitFodFSo\nTq2gtICBKwZSUl4CgE8zH0kcwukYDNCnD6xbB2vXagnk9tu1fpKcHL2jE47gsKuQl5cXS5YsoX//\n/kRGRjJixAhiYmJYunSp9ZHcZ555hk2bNhEaGkrv3r15/vnn8fX1JTc3l+7duxMVFUV0dDR33XUX\nQ4cOdVSoTq2FZwtS4lPw8vDSOxQhaiQyEt5/H9LT4fJlbSbf8ePlMV9XI4MEndCGrA0cPnuYCdET\n9A5FiFo7exZee02bSysyUpuIsV8/rVoR+rDHtVOShxM6lH+I3MJc4trF6R2KEHZTWgoffKBNDe/h\noSWR+++Hxo31jqzhkeThQsljecZy7g25l1ZNWukdihAOpRR8842WRH76SXv0d/JkbeEqUTecusNc\n2KbwciEXSi/oHYYQDmcwwIAB8O23Wuf6oUPQoYO21vpPP+kdnagpqTx0UlJewpbsLfRp30fvUITQ\nXW6u1i/y2msQHa2tLZKQIP0ijiKVRz2WX5zP+3ver7fJTwh7uvVWSEnRpoC/7z5tiVyjUUsmRUV6\nRyeuRSqPOnT20lkqLBX4NPPROxQhnJpSsH69tlDVhg3aVCiPPgrt2+sdmWuQyqOeeW3Ha6w5tKb6\nHYVo4AwGiI/X5szasQPc3KBrVxg+HL7/XiZjdAZSeTjYuUvnrE9QKaVkUkEhblBREaxYoVUjFotW\niYwbp834K2wjlYeTK7eU0/vt3uQV5QE1myxSCHFtzZrBww9DZiYsWaLNpdWuHTz2mDylpQepPByg\n3FKOh5s22+3liss0dpdRUEI4gtmsLZX7+usQGqpVI0OHarP+iqrJIEEnTB7fHv2Wt3a9xYoRK/QO\nRYgG4/Jl+Pxz+L//g6NHYdIkeOghCAjQOzLnJMnDCZNHuaWci6UXZaS4EDrJzNQe8f3wQ7jrLpgy\nRZtLy01u0ltJ8nCS5DF59WRGh4/mrqC79A5FCPGrwkKtg33JEu31ww9rj/z6+uodmf4keThJ8jiU\nf4iglkE0cpcbrUI4G6Vg2zatX+TLL+Gee7RE0qdPw61G5GkrnZwqPMWDKx+kwlIBQMebO0riEMJJ\nGQxw552wfDkcOwa9e2vTn3TurK18ePq03hHWT5I8boBvM18eCH9AVvQTop5p2VJ7Imv3bnjvPe0R\n306dYNQoSE2Figq9I6w/HHr1S01NJTw8nNDQUObNm3fV9tzcXPr27YvRaKRz587WFQZrcmxdW31w\nNasPrga0ku+e4Htk3IYQ9dSVauSttyArS5uE8emntelPUlK0z8T1OazPo7S0lJCQEDZt2oSfnx/d\nu3fn9ddfJzo62rrP3//+dyoqKpg7dy5nzpyhY8eO5ObmAlR7LNRtn0daThpuBjfuaHtHnXyfEKLu\n7doFb76pLVoVGwsTJ8KwYeDpqXdk9uXUfR7btm3DaDTi7++Ph4cHSUlJrFlTeV6nwMBACgoKACgo\nKMDHxwdPT88aHetoFZYKFv64kNLyUgC6+neVxCGEi4uK0pbLNZu1qU9ef10bK/I//wMZGXpH51wc\nljzMZjOBgYHW9wEBAZjN5kr7PPTQQ+zbt4+2bdsSGRnJokWLanyso7kZ3LAoC8VlxXX6vUII/TVp\nAg88oC1YlZYGt9wC996rrTXyyiuQn693hPrzcNSJa9IfMGfOHKKiojCZTBw5coSEhAR2795tU19C\nSkqK9XV8fDzx8fE3EO3VDAYDM3rOsMu5hBD1V1AQzJql9Yn88IPWT/LMM9C3Lzz4ICQmOv90KCaT\nCZPJZNdzOix5BAQEkJ2dbX2fnZ1dqZoA2LRpE08//TQAwcHBtG/fnv3799fo2Ct+mzyEEMJR3Ny0\nhNG3L1y4AJ9+CvPna9OgJCdriSQqyjlXP/z9H9azZ8+u9Tkddtuqa9eu7N27l5ycHMrKyvjkk09I\nTEystE9wcDDffvstAKdOnWL//v20b9++RscKIYRebrpJmz9r40bYvBlatNBua0VGwpYtekdXNxw6\nwnzdunXMmDEDi8XC2LFjmTlzpvVx3MmTJ3Pq1CnGjBlDTk4OFRUVPPnkk0ycOLHKY68K3klGmAsh\nhMWirXrYsSP4++sdzfXJ9CSSPIQQwmZO/aiuEEII1yXJQwghhM0keQghhLCZJA8hhBA2k+QhhBDC\nZpI8hBBC2EyShxBCCJtJ8hBCCGEzSR5CCCFsJslDCCGEzSR5CCGEsJkkDyGEEDaT5CGEEMJmkjyE\nEELYTJKHEEIImzk0eaSmphIeHk5oaCjz5s27avuCBQuIjo4mOjqa8PBwPDw8OH/+PABBQUFEREQQ\nHR1Nt27dHBmmEEIIWykHKSkpUUFBQcpsNquysjIVGxur0tPTq9x/9erVqm/fvtb3QUFBKj8//7rf\n4cDwncIPP/ygdwgOJe2rv1y5bUq5fvvsce10WOWxbds2jEYj/v7+eHh4kJSUxJo1a6rc/4MPPiA5\nOfn3ic1R4dULJpNJ7xAcStpXf7ly28D122cPDkseZrOZwMBA6/uAgADMZvM19y0uLuabb75h5MiR\n1s8MBgMJCQlERESwePFiR4UphBDiBng46sQGg6HG+65evZpevXrRsmVL62dbt27F19eXvLw8BgwY\nQEhICP369XNEqEIIIWxV+7tn17ZhwwY1aNAg6/sXXnhBPf/889fcd/jw4erDDz+s8lxz5sxRc+bM\nuerz4OBgBciP/MiP/MiPDT/BwcG1vsYblHJMx0JJSQkhISFs3rwZX19fevTowdKlS4mJiam034UL\nF7j99tsxm800adIE0G5jATRt2pSioiIGDhzI9OnTGTp0qCNCFUIIYSOH3bby8vJiyZIl9O/fH4vF\nwtixY4mJiWHp0qUATJ48GYCVK1fSv39/a+IAOHXqFMOHD8dgMFBcXMz9998viUMIIZyIwyoPIYQQ\nrstpR5hXN8DwwIEDdO/eHS8vLxYuXFhp26xZs+jUqRMhISGMGjXKehvMWVTXtvfee4+IiAjCw8OJ\njY1l586dNT7WGdxo+7Kzs+nduzfh4eF07tyZF154oa5Dr5Ha/PcDqKioIDo6miFDhtRVyDapTfvO\nnz/PfffdR2RkJF26dGHLli11GXqN1KZ9zn5tgerbt2rVKiIiIoiMjCQ8PJzU1NQaH1tJrXtNHKAm\nAwxPnz6t0tLS1FNPPaUWLFhg/fzQoUOqffv2qrS0VCml1B//+Ee1bNmyOo3/emrStm3btqmCggKl\nlFLr1q1TUVFRNT5Wb7VpX25ursrMzFRKKXXx4kXVsWNHtWvXrrptQDVq074rFi5cqEaPHq2GDBlS\nZ3HXVG3bN2rUKPXBBx8opZSqqKhQFy5cqLvga6A27XP2a4tSNWtfYWGh9fWePXvUbbfdVuNjf8sp\nK4+aDDD08fEhNjaWRo0aVfq8devWNGrUiKKiIsrLyykuLqZdu3Z1Gf511aRt3bp1o3nz5gD07NmT\nnJycGh+rt9q0z8/Pj7CwMAC8vb2JiIjgl19+qdsGVKM27QNt/NPatWuZNGmSUw6CrU378vPz2bVr\nl3Wwr5ubGy1atKjbBlSjNu1z9msL1Kx9zZo1s74uLCykTZs2NT72t5wyedgywPD3WrduzfTp07nt\nttto27YtLVu2dKrxIba2benSpQwbNuyGjtVDbdr3W8ePHyctLY1evXo5JM4bVdv2PfHEE8yfPx83\nN6f8p1er9h06dAgfHx/++Mc/EhYWxrhx4ygsLHR4zLaoTfuc/doCNW/fypUr6dKlC4mJibzyyis2\nHXuFU/4G2zLA8PeOHDnCyy+/zPHjx/nll18oLCxkxYoVdoyudmxpm8lkYvny5dZ7/7X5/6Wu1KZ9\nVxQWFnLfffexaNEi61+AzqI27fv666/x9fUlOjraKasOqF37LBYLaWlpzJgxg71799K6dWuee+45\nR4V6Q2rTPme/tkDN2zd8+HB++uknVq9ezdixY2/o99Epk0dAQADZ2dnW99nZ2ZUy4vVs376dHj16\ncPPNN+Ph4cGIESPYtGmTo0K1WU3btmfPHiZNmsRXX31Fq1atbDpWT7VpH0BZWRkjR45k9OjRDB8+\nvE5itkVt2vfjjz/y1Vdf0b59e5KTk/n+++8ZN25cncVeE7VpX2BgIP7+/nTt2hWAUaNGsWvXrroJ\nvIZq0z5nv7aA7deIuLg4ysvLOX36NIGBgbZdXxzQZ1Nrly5dUu3atVNms1ldvnxZxcbGqp07d15z\n31mzZlXqMN++fbsyGo2quLhYWSwWNW7cuErb9VaTtmVlZang4GC1ZcsWm4/VW23aZ7FY1NixY9W0\nadPqMmSb1KZ9v2UymdTgwYMdHa7Natu+O+64Qx08eFAppf3b/POf/1wncddUbdrn7NcWpWrWvmPH\njllf79y5UwUEBCiLxWLz9cUpk4dSSq1du1YZjUbVpUsX69Qkr732mnrttdeUUkqdPHlSBQQEqBYt\nWqiWLVuqwMBAdfHiRaWU9kvboUMH1alTJ5WUlKQuXbqkWzuupbq2TZw4UbVu3VpFRUWpqKgo1bVr\n1+se62xutH0bN25UBoNBRUZGWretW7dOt3ZUpTb//a4wmUxO+bSVUrVr365du1RsbKwKDQ1ViYmJ\n6uzZs7q04Xpq0z5nv7YoVX375s6dq8LCwlRYWJjq2rWr2rRp03WPrYoMEhRCCGEzp+zzEEII4dwk\neQghhLCZJA8hhBA2k+QhhBDCZpI8hBBC2EyShxBCCJtJ8hBCCGEzSR7CZfXs2bPKbcePH6dJkybE\nxMQQExPDnXfeyTvvvHPNfd9++218fHx4+OGHHRWqzQYNGkRBQQEXLlxgyZIl1s+PHj1KVFSU080J\nJlyPDBIUDdLx48cZMmQImZmZAJw8eZJhw4YxdepU/vSnP1Xa95133mHnzp3W2Udry2Kx2G1W3d+3\n44rmzZtz8eJFu3yHENcilYdwWd7e3jXet02bNixatKjKBPHbv7Hefvtthg0bRr9+/ejQoQNPPfWU\nddsbb7xBZGQkRqORCRMmUF5ebo3lL3/5C7GxsWzbtq3SuePj462r1Z05c4b27dtbv2fEiBEMHjyY\n22+/nWnTplmPCQoKIj8/n7/+9a8cOXKE6OhonnzyyRq3V4ja8tA7ACEcxdYp7KOjozlw4ECN9k1L\nS+PAgQN4eXkRGxvL4MGDadq0KatWrSI9PR13d3emTp3K22+/zaRJkyguLqZnz54sWLDgmnFWFevu\n3bvJzMzE3d2dTp06MW3aNIKCgqzHzJs3j3379pGRkWFTW4WoLUkeQvzKlju499xzj3WVvHvvvZdN\nmzbh5uZGRkYGsbGxAFy6dAkfHx8A3N3db2iK+b59+9K0aVMAjEYjZrOZoKCgG4pZCHuS5CHErzIy\nMujSpUu1+/2+SlBKYTAYUEoxceJEnn322auO8fLyqrK6cHNzw2KxAFBSUlJpm6enp/W1u7u7dT8h\n9CZ9HkKgdZhPnz6dxx9/vNp9lVL85z//oaCggMuXL7Nq1Sp69epFQkICn3zyCefOnQOgoKCgRssE\nBwQEsGPHDgC+/PJLm+Ju0qQJxcXFNh0jhD1I5SFcVnV9HkeOHCEmJgalFJ6enkydOrVGK/sZDAa6\ndevGyJEjOXbsGElJSdx5550AzJw5k7i4ODw8PHBzc+O1114jICDgurHMmDGDkSNH8uabbzJgwADr\nvtfrC7nCz8+PqKgoQkNDGTJkCPPmzas2fiHsQR7VFaIa77zzDjt27ODVV18FtKegdu7caX3vjORR\nXeFocttKiGo0adKEdevWWQcJ1qQi0MuVQYK33nqr3qEIFyeVh3BpmZmZV92K8vLyYsuWLTpFJIRr\nkOQhhBDCZnLbSgghhM0keQghhLCZJA8hhBA2k+QhhBDCZpI8hBBC2Oz/A7Fpp7L/E1W7AAAAAElF\nTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x31d4f90>" + ] + } + ], + "prompt_number": 14 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter2.ipynb b/ELECTRIC_MACHINERY/chapter2.ipynb new file mode 100755 index 00000000..855c3bd6 --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter2.ipynb @@ -0,0 +1,761 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 2: Transformers" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.1, Page number: 63" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "\n", + "#Variable declaration\n", + "Pc=16 #Core loss at Bmax=1.5 T\n", + "VIrms=20 #Voltamperess for the core\n", + "Vrms=194 #Rms induced voltage(V)\n", + "\n", + "\n", + "#Calculation:\n", + "pf=Pc/VIrms\n", + "a=math.acos(pf)\n", + "I=VIrms/Vrms\n", + "Ic=I*pf\n", + "Im=I*math.fabs(math.sin(a))\n", + "\n", + "#Results:\n", + "print \"Power factor = \", round(pf,1),\"lagging\"\n", + "print \"The core-loss current,Ic =\", round(Ic,3), \"A rms\"\n", + "print \"The magnetising current,Im =\", round(Im,2),\"A rms\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Power factor = 0.8 lagging\n", + "The core-loss current,Ic = 0.082 A rms\n", + "The magnetising current,Im = 0.06 A rms\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.2, Page number: 67" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declarations:\n", + "k=5 #turns ratio,N1/N2\n", + "Z2=1+4j #Impedance of secondary side(ohm)\n", + "Vp=120 #primary voltage(V)\n", + "\n", + "#Calculations:\n", + "Z2p=k**2*(Z2)\n", + "I=Vp/Z2p\n", + "Is=k*I\n", + "\n", + "#Results:\n", + "print \"Primary current:\",complex(round(I.real,2),round(I.imag,2)), \"A rms\"\n", + "print \"Current in the short:\",round(Is.real,2)+1j*round(Is.imag,2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Primary current: (0.28-1.13j) A rms\n", + "Current in the short: (1.41-5.65j) A\n" + ] + } + ], + "prompt_number": 53 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.4, Page number: 74" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "\n", + "#Variable declaration:\n", + "R1=0.72 #Resistance at high voltage side(ohm)\n", + "R2=0.70 #Resistance at low voltage side(ohm)\n", + "X1=0.92 #Reactance at high voltage side(ohm)\n", + "X2=0.90 #Reactance at low voltage side(ohm)\n", + "Zq=632+4370j #Impedance of exciting circuit(ohm)\n", + "\n", + "#Calculations:\n", + "Req=R1+R2\n", + "Xeq=X1+X2 \n", + "Vcd=2400*Zq/(Zq+complex(R1,X1))\n", + "V=complex(round(Vcd.real,2),round(Vcd.imag,3))\n", + "\n", + "#Results:\n", + "print \"Req:\",Req,\"ohm\",\" and Xeq:\",Xeq,\"ohm\"\n", + "print \"Voltage at low voltage terminal:\",V,\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Req: 1.42 ohm and Xeq: 1.82 ohm\n", + "Voltage at low voltage terminal: (2399.45+0.316j) V\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.5, Page number: 76" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "\n", + "#Variable declaration:\n", + "Zf=0.30+.160j #Impedance of feeder(ohm)\n", + "Zeq=1.42+3.42j #Equiv.impedance of transformer refd. to primary(ohm)\n", + "k=2400/240 #turns ratio\n", + "P=50000 #power rating of the transformer(VA)\n", + "Vs=2400 #sending end vltage of feeder(V)\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "I=P/2400 #Rated current(A)\n", + "theta=math.acos(0.80)\n", + "Zt=Zf+Zeq #combned impedance of feeder & transformer(ohm)\n", + "R=Zt.real\n", + "X=Zt.imag\n", + "bc=I*X*math.cos(theta)-I*R*math.sin(theta)\n", + "ab=I*R*math.cos(theta)+I*X*math.sin(theta)\n", + "Ob=(Vs**2-bc**2)**0.5\n", + "V2=Ob-ab\n", + "\n", + "\n", + "#Results:\n", + "print \"The voltage at the secondary terminals:\",round(V2/10,0),\"V\\n\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The voltage at the secondary terminals: 233.0 V\n", + "\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.6, Page number: 80" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "import math\n", + "\n", + "#Variable declaration:\n", + "#short ckt test readings:\n", + "Vsc=48 #voltage(V)\n", + "Isc=20.8 #current(A)\n", + "Psc=617 #power(W)\n", + " \n", + "#Open ckt test readings:\n", + "Vs=240 #Voltage(V)\n", + "I=5.41 #current(A)\n", + "P=186 #power(W)\n", + "V2ph=2400 #voltage at full load at high voltage side(V)\n", + "pf=0.8 #lagging power factor at full load\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "theta=math.acos(pf)\n", + "Zeqh=Vsc/Isc #subscript h refers to high voltage side\n", + "Reqh=Psc/Isc**2\n", + "Xeqh=math.sqrt(Zeqh**2-Reqh**2)\n", + "Ih=50000/V2ph\n", + "Pout=50000*pf\n", + "Pwind=Ih**2*Reqh\n", + "Ptloss=P+Pwind\n", + "e=(1-Ptloss/(Ptloss+Pout))*100\n", + "Iph=(50000/2400)*complex(math.cos(theta),math.sin(-theta))\n", + "V1ph=V2ph+Iph*complex(Reqh,Xeqh)\n", + "r=(round(abs(V1ph),2)-2400)*100/V2ph\n", + "\n", + "\n", + "#Results:\n", + "print \"The efficiency of the transformer:\",round(e,0),\"%\"\n", + "print \"Volatge Regulation:\",round(r,2),\"%\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The efficiency of the transformer: 98.0 %\n", + "Volatge Regulation: 1.94 %\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.7, Page number: 82" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Varaible declaration:\n", + "Vx=2400 #Voltage at low voltage side(V)\n", + "Vbc=2400 #Voltage across branch bc(V)\n", + "Vab=240 #Voltage induced in winding ab(V)\n", + "Pl=803 #transformer losses(W)\n", + "pf=0.8 #Power factor of the transformer\n", + "\n", + "#Calculations:\n", + "Vh=Vab+Vbc\n", + "Ih=50000/Vab\n", + "KVA=Vh*Ih/1000 #Kva rating\n", + "P=pf*550000\n", + "e=(1-Pl/(P+Pl))*100\n", + "\n", + "\n", + "#Results:\n", + "print \"Voltage ratings, Vh:\",Vh,\"V \", \"& Vx:\",Vx,\"V\"\n", + "print \"KVA rating as an autotransformer:\",KVA,\"KVA\"\n", + "print \"full-load efficiency:\", round(e,2),\"%\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Voltage ratings, Vh: 2640 V & Vx: 2400 V\n", + "KVA rating as an autotransformer: 550.0 KVA\n", + "full-load efficiency: 99.82 %\n" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.8, Page number: 87" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "\n", + "#Variable declaration:\n", + "Vl1=4160 #line-to-line voltage at feeder's sending end(V)\n", + "Zf=0.30+.160j #Impedance of feeder(ohm)\n", + "Zeq=1.42+3.42j #Equiv.impedance of transformer refd. to primary(ohm)\n", + "k=2400/240 #turns ratio\n", + "P=50000 #power rating of the transformer(VA)\n", + "Vs=2400 #sending end vltage of feeder(V)\n", + "\n", + "\n", + "\n", + "#Calculation:\n", + "#this problem can be treated on a single phase basis,\n", + "#and whole problem is similar to Ex 2.5.\n", + "\n", + "I=P/2400 #Rated current(A)\n", + "theta=math.acos(0.80)\n", + "Zt=Zf+Zeq #combned impedance of feeder & transformer(ohm)\n", + "R=Zt.real\n", + "X=Zt.imag\n", + "bc=I*X*math.cos(theta)-I*R*math.sin(theta)\n", + "ab=I*R*math.cos(theta)+I*X*math.sin(theta)\n", + "Ob=(Vs**2-bc**2)**0.5\n", + "V2=Ob-ab\n", + "Vload=V2/k\n", + "\n", + "#Results:\n", + "print \"The line to line voltage:\",round(Vload,0),\"V line-to-line\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The line to line voltage: 233.0 V line-to-line\n" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.9, Page number: 89" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "import cmath\n", + "\n", + "#Variable decclaration:\n", + "#All resistances, reactances, & impedances are on per phase basis\n", + "Req=1.42 #Series resist. of del-del transformer referred to 2400v side(ohm)\n", + "Xeq=1.82 #Series react. of del-del transformer referred to 2400v side(ohm)\n", + "Zs=0.17+0.92j #Equiv impedance of sending end transformer(ohm)\n", + "Xf=0.8j #Reactance of the feeder(ohm)\n", + "Vf=2400 #Voltage of the feeder(V)\n", + "k=10 #turns ratio(Vp/Vs)\n", + "\n", + "\n", + "#Calculations:\n", + "Zt=(complex(Req,Xeq)/3)+Zs+Xf\n", + "Ztot=complex(round(Zt.real,2),round(Zt.imag,2))\n", + "If=math.floor(Vf/(math.sqrt(3))/round(abs(Ztot),2))\n", + "I1=If/math.sqrt(3)\n", + "I2=I1*k\n", + "Ic=I2*math.sqrt(3)\n", + "\n", + "\n", + "#Results:\n", + "print \"Short circuit current in the 2400 feeder, per phase wires:\",round(Ic,1),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Short circuit current in the 2400 feeder, per phase wires: 5720.0 A\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.10, Page number: 92" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "X1=143 #Reactance of primary(ohm)\n", + "X21=164 #Reactance of secondary ref. to primary(ohm)\n", + "Xm=163*10**3 #Reactance of magnetising ckt(ohm)\n", + "R1=128 #Resistance of primary(ohm)\n", + "R21=141 #Resistane of secondary ref. to primary(ohm)\n", + "k=20 #turns ratio(2400/120)\n", + "V1=2400 #primary voltage(V)\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "V2=(V1/k)*complex(0,Xm)/complex(R1,X1+Xm)\n", + "mag=abs(V2)\n", + "ph=degrees(cmath.phase(V2))\n", + "\n", + "\n", + "#Results:\n", + "print \"Magnitude of V2:\",round(mag,2),\"V\"\n", + "print \"Phase of V2:\",round(ph,3),\"degrees\"\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Magnitude of V2: 119.89 V\n", + "Phase of V2: 0.045 degrees\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.11, Page number: 94" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "\n", + "\n", + "#variable declaration:\n", + "X1=44.8*10**-6 #Reactance of the primary(ohm)\n", + "R1=10.3*10**-6 #Resistance of the primary(ohm)\n", + "X21=54.3*10**-6 #Reactance of the secondary refd. to primary(ohm)\n", + "R21=9.6*10**-6 #Resistance of secondary ref. to primary(ohm)\n", + "Xm=17.7*10**-3 #Reactance of the magnetising ckt(ohm)\n", + "k=5/800 #turms ratio(I2/I1)\n", + "Zl=2.5+0j #Impedance ofthe load(ohm)\n", + "I1=800 #primary current(A)\n", + "\n", + "#Calculations:\n", + "Zp=k**2*Zl\n", + "I2=I1*k*Xm*1j/(Zp+R21+(X21+Xm)*1j)\n", + "phase=cmath.phase(I2)\n", + "\n", + "\n", + "#Results:\n", + "print \"Magnitude of current:\",round(abs(I2),2),\"A\"\n", + "print \"Phase of the current:\",round(math.degrees(phase),3),\"degrees\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Magnitude of current: 4.98 A\n", + "Phase of the current: 0.346 degrees\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.12, Page number: 97" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "\n", + "#Variable declaration:\n", + "XL=0.040 #Reactance at l.v side(ohm)\n", + "XH=3.75 #Reactance at h.v side(ohm)\n", + "Xm=114 #Magnetising reactance(ohm)\n", + "RL=0.76*10**-3 #Resistance at l.v.side(ohm)\n", + "RH=0.085 #Resistance at l.v.side(ohm)\n", + "VA_base=100*10**6 #base VA\n", + "V_base=7.97*10**3 #base voltage(V)\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "#for l.v side\n", + "VA_base=100*10**6 #base VA\n", + "V_base=7.97*10**3 #base voltage(V)\n", + "Rbase1=Xbase1=V_base**2/VA_base\n", + "\n", + "#for h.v side:\n", + "VA_base=100*10**6 #base VA\n", + "V_base=79.7*10**3 #base voltage(V)\n", + "Rbase2=Xbase2=V_base**2/VA_base\n", + "\n", + "XL_pu=XL/Xbase1\n", + "XH_pu=XH/Xbase2\n", + "Xm_pu=Xm/Xbase1\n", + "RL_pu=RL/Rbase1\n", + "RH_pu=RH/Rbase2\n", + "K_pu=1 #per unit utrns ratio\n", + "\n", + "#Results:\n", + "print \"The per unit parameters are:\"\n", + "print \"XL_pu =\",round(XL_pu,3),\"p.u\"\n", + "print \"XH_pu =\",round(XH_pu,4),\"p.u\"\n", + "print \"Xm_pu =\",math.ceil(Xm_pu),\"p.u\"\n", + "print \"RL_pu =\",round(RL_pu,4),\"p.u\"\n", + "print \"XL_pu =\",round(RH_pu,4),\"p.u\"\n", + "print \"Turns ratio =\",K_pu,\"p.u\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The per unit parameters are:\n", + "XL_pu = 0.063 p.u\n", + "XH_pu = 0.059 p.u\n", + "Xm_pu = 180.0 p.u\n", + "RL_pu = 0.0012 p.u\n", + "XL_pu = 0.0013 p.u\n", + "Turns ratio = 1 p.u\n" + ] + } + ], + "prompt_number": 59 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.13, Page number: 98" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "\n", + "\n", + "#Variable declaration:\n", + "Ic=5.41 #Exciting current ref. to low volt. side(A)\n", + "k=10 #turns ratio(N1/N2=2400/240)\n", + "Vbh=2400 #base voltage at primary side(V)\n", + "Vbl=240 #base voltage at secondary side(V)\n", + "Ibh=20.8 #base current at primary side(A)\n", + "Ibl=208 #base current at secondary side(A)\n", + "Z=1.42+1.82j #Equiv.impedance ref.to high voltage side(ohm)\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "Zbh=Vbh/Ibh\n", + "Zbl=Vbl/Ibl\n", + "Icl=Ic/Ibl\n", + "Ich=Ic/(Ibh*k)\n", + "Zl=Z/(k**2*Zbl)\n", + "Zh=Z/Zbh\n", + "\n", + "\n", + "#Results:\n", + "print \"Per unit exciting current on low volt. sides:\",round(Icl,3,),\"A\" \n", + "print \"Per unit exciting current on high volt. sides:\",round(Ich,3),\"A\"\n", + "print \"per unit equiv.impedance at low volt. sides:\",round(Zl.real,4)+round(Zl.imag,4)*1j,\"ohm\"\n", + "print \"per unit equiv.impedance at high voltage sides:\",round(Zh.real,4)+round(Zh.imag,4)*1j,\"ohm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Per unit exciting current on low volt. sides: 0.026 A\n", + "Per unit exciting current on high volt. sides: 0.026 A\n", + "per unit equiv.impedance at low volt. sides: (0.0123+0.0158j) ohm\n", + "per unit equiv.impedance at high voltage sides: (0.0123+0.0158j) ohm\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.14, Page number: 100" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "\n", + "#Variable declaration:\n", + "Vb=24000 #Base voltage of secondary of sending end transformer(V) \n", + "Z=0.17+0.92j #Impedance of sending end transformer ref. to 2400V side(ohm)\n", + "P=150 #Power rating of the transformer(KVA)\n", + "V=2400 #Primary voltage of sending end transformer(v)\n", + "Ztot=0.64+2.33j #Total series impedance(ohm)\n", + "\n", + "#Calculations:\n", + "Zb=V**2/(P*10**3)\n", + "Ztotb=Ztot/Zb\n", + "Vsb=1 #Vs in terms of per unit values\n", + "Isc=Vsb/abs(Ztotb) #Short current in per unit values(A)\n", + "Ib1=P*10**3/(sqrt(3)*2400) #base current of the feeder at 2400V side(A)\n", + "If=Ib1*Isc\n", + "Ib2=P*10**3/(sqrt(3)*240)\n", + "Iscs=Isc*Ib2 #short ckt current at 2400V afeeder side (A)\n", + "\n", + "#Results:\n", + "print \"Short circuit current in 2400 feeder:\",round(Iscs/10**3,2),\"KA\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Short circuit current in 2400 feeder: 5.73 KA\n" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 2.15, Page number: 102" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "P=250*10**3 #power rating of transformer(KVA)\n", + "Vp=2400 #primary volatge(V)\n", + "Vs=460 #secondary voltage(V)\n", + "Pb=100*10**3 #new base power of transformer(KVA)\n", + "Vb=460 #new base voltage(V)\n", + "Z=0.026+0.12j #series impedance on its own base(ohm)\n", + "Vl=438 #load voltage(V)\n", + "Pl=95*10**3 #power drawn by the load(kW)\n", + "\n", + "#Calculations:\n", + "Zbo=Vs**2/P #base impedance for the transformer(ohm)\n", + "Zbn=Vb**2/Pb #base impedance for the transformer at 100KVA base(ohm)\n", + "Zpn=Z*Zbo/Zbn #base impedance at 100KVA base(ohm)\n", + "Vpl=Vl/Vb #per unit load voltage(V)\n", + "Ppl=Pl/Pb #per unit load power\n", + "Ipl=Ppl/Vpl #per unit load current(A)\n", + "Vpp=Vpl+Ipl*Zpn #high side voltage of the transformer(V) \n", + "\n", + "\n", + "#Results:\n", + "print \"The high side voltage:\",round(abs(Vpp*Vp),0),\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The high side voltage: 2313.0 V\n" + ] + } + ], + "prompt_number": 14 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter3.ipynb b/ELECTRIC_MACHINERY/chapter3.ipynb new file mode 100755 index 00000000..abe5516c --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter3.ipynb @@ -0,0 +1,460 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 3: Electromechanical-Energy-Conversion-Principles " + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.1, Page number: 114" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "I=10 #current in the coil(A)\n", + "Bo=0.02 #magnetic field (T)\n", + "R=0.05 #radius of the rotor(m)\n", + "l=0.3 #rotor length(m)\n", + "\n", + "\n", + "#Calculations:\n", + "q=symbols('q') #Direction of torque\n", + "F1=-2*I*l*Bo*sin(q) #Force on the coil(N)\n", + "T=F1*R #Torque scting in theta direction(Nm)\n", + "\n", + "\n", + "#Results:\n", + "print \"Force per unit length:\",T,\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Force per unit length: -0.006*sin(q) Nm\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.2, Page number: 121" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "\n", + "#Variable declaration\n", + "N=1000 #No of winding turns\n", + "g=2 #Air gap width(mm)\n", + "d=0.15 #Magnetic core width,d (m)\n", + "l=0.1 #thickness of core(0.1)\n", + "x,d=symbols('x d') #where h is height of plunger(m) \n", + " #Lx is inductance as a function of x(H)\n", + "i=10 #Current in the winding(A)\n", + "uo=4*3.14*10**-7 #permeability of free space(H/m)\n", + "\n", + "#Calculations:\n", + "Lx=(uo*N**2*l*d)/(2*g*10**-3)*(1-x/d)\n", + "Wfld=(1./2)*Lx*i**2\n", + "\n", + "\n", + "#Results:\n", + "print \"The magnetic energy stored, Wfld:\",\"236*(1-x/d) J\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The magnetic energy stored, Wfld: 236*(1-x/d) J\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.3, Page number: 124" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "import numpy as np\n", + "\n", + "#Variable declaration:\n", + "xdata=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0] #(cm)\n", + "Ldata=[2.8, 2.26, 1.78, 1.52, 1.34, 1.26, 1.20, 1.16, 1.13, 1.11, 1.10] #(mH)\n", + "I = 0.75 #(A)\n", + "\n", + "\n", + "#Calculations:\n", + "x=0.01*np.array(xdata)\n", + "L=0.001*np.array(Ldata)\n", + "length=len(x)\n", + "xmax=x[length-1]\n", + "a=polyfit(x,L,4)\n", + "xfit=[0]*102\n", + "Lfit=[0]*102\n", + "for n in range(1,102,1):\n", + " xfit[n-1]=xmax*(n-1)/100\n", + " Lfit[n-1]=a[0]*xfit[n-1]**4+a[1]*xfit[n-1]**3+a[2]*xfit[n-1]**2+a[3]*xfit[n-1]+a[4]\n", + "\n", + "#Plot the data and then the fit to compare (convert xfit to cm and Lfit to mH)\n", + "plot(xdata,Ldata,'o')\n", + "plot(100*np.array(xfit),1000*np.array(Lfit),'g.')\n", + "xlabel('x [cm] ')\n", + "ylabel('L [mH] ')\n", + "title('Inductance,L vs length,l')\n", + "grid()\n", + "print \"The required plots are shown below:\"\n", + "show()\n", + "\n", + "#set current to 0.75 A\n", + "I=0.75\n", + "F=[0]*102\n", + "for n in range(1,102,1):\n", + " xfit[n-1]=0.002+0.016*(n-1)/100\n", + " F[n-1]=4*a[0]*xfit[n-1]**3+3*a[1]*xfit[n-1]**2+2*a[2]*xfit[n-1]**1+a[3]\n", + " F[n-1]=(I**2/2)*F[n-1]\n", + "plot(100*np.array(xfit),F,'b.')\n", + "xlabel('x [cm]')\n", + "ylabel('Force [N]')\n", + "title('Force, F vs length,l')\n", + "grid()\n", + "\n", + "#Results:\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "The required plots are shown below:" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['Polygon', 'poly', 'sign', 'flatten', 'conjugate', 'diff', 'tan', 'Circle', 'roots', 'plot', 'eye', 'trace', 'floor', 'diag', 'invert', 'nan', 'sqrt', 'source', 'add', 'zeros', 'take', 'var', 'pi', 'plotting', 'product', 'seterr', 'power', 'multinomial', 'transpose', 'test', 'beta', 'ones', 'sinh', 'vectorize', 'cosh', 'trunc', 'cos', 'prod', 'tanh', 'mod', 'det', 'sin', 'binomial', 'solve', 'log', 'exp', 'reshape', 'gamma', 'interactive']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x3dc9590>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEZCAYAAACw69OmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noRARkSSYUIiISBL/D5upbph/P2HSAAAAAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x48a36d0>" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.4, Page number: 128" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "Lo=10.6*10**-3 #Initial inductance(H)\n", + "L2=2.7*10**-3 #H\n", + "\n", + "\n", + "#Calculations:\n", + "theta,i=symbols('theta i')\n", + "L=Lo+L2*cos(2*theta)\n", + "i=2 #Coil current,A\n", + "def T(theta):\n", + " return i**2*diff(L,theta)/2\n", + " \n", + "\n", + "#Results:\n", + "print \"Torque,Tfld =\",T(theta),\" N.m\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Torque,Tfld = -0.0108*sin(2*theta) N.m\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.6, Page number: 134" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "r1=2.5*10**-2 #radius of rotor(m)\n", + "h=1.8*10**-2 #Axial length(m)\n", + "g=3*10**-3 #Air gap length(m)\n", + "Bag=1.65 #Magnetic field(T)\n", + "uo=4*pi*10**-7 #permeability of free space(H/m)\n", + "\n", + "#Calculations:\n", + "H=Bag/uo\n", + "Ni=2*g*H\n", + "T=uo*(Ni)**2*h*(r1+0.5*g)/(4*g)\n", + "\n", + "#Results:\n", + "print \"The maximum torque:\", round(T,2),\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The maximum torque: 3.1 Nm\n" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.7, Page number: 140" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from matplotlib import *\n", + "\n", + "#Variable declaration:\n", + "i1=0.8\n", + "i2=0.01\n", + "\n", + "\n", + "#Calculations & Results:\n", + "def df(f,x,h=0.1e-10):\n", + " return ( f(x+h/2) - f(x-h/2) )/h\n", + "\n", + "\n", + "\n", + "def l11(x):\n", + " return (3+cos(2*x))/1000.0\n", + "\n", + "def l12(x):\n", + " return (0.3*cos(x))\n", + "\n", + "def l22(x):\n", + " return (30+10*cos(2*x))\n", + "\n", + "def g(x):\n", + " return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x) + (i1*i2)*df(l12,x)\n", + "\n", + "def r(x):\n", + " return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x)\n", + "def s(x):\n", + " return (i1*i2)*df(l12,x)\n", + "\n", + "x=linspace(-pi,pi,100000)\n", + "\n", + "\n", + "plot(x,r(x))\n", + "plot(x,s(x))\n", + "plot(x,g(x))\n", + "grid()\n", + "annotate(\"Total torque\",xy=(-0.5,0.003))\n", + "annotate(\"Reluctance torque\",xy=(-2,-0.0015))\n", + "annotate(\"Mutual Interaction torque\",xy=(1.6,-0.0026))\n", + "xlabel(\"Theta [radians]\")\n", + "ylabel(\"Torque [N.m]\")\n", + "xlim(-pi,pi)\n", + "\n", + "\n", + "#Results\n", + "print \"Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)\"\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['vectorize', 'prod', 'plotting', 'Circle', 'diag', 'sinh', 'trunc', 'plot', 'eye', 'det', 'tan', 'product', 'gamma', 'roots', 'sin', 'zeros', 'cosh', 'interactive', 'conjugate', 'take', 'trace', 'beta', 'exp', 'ones', 'multinomial', 'cos', 'transpose', 'solve', 'diff', 'invert', 'pi', 'tanh', 'Polygon', 'reshape', 'sqrt', 'floor', 'source', 'add', 'poly', 'mod', 'sign', 'power', 'binomial', 'log', 'var', 'seterr', 'flatten', 'nan', 'test']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAcAAAAEPCAYAAADVmxQSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns/T/jVWB3070t1UAAAAASUVORK5CYII=\n", + "text": [ + "<matplotlib.figure.Figure at 0x3dbe950>" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 3.9, Page number: 148" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "\n", + "#Variable declaration:\n", + "W=4.0*10**-2 #width of plunger lower arm(m)\n", + "W1=4.5*10**-2 #width of plunger upper arm(m)\n", + "D=3.5*10**-2 #depth of plunger (m)\n", + "d=8*10**-3 #length of magnet(m)\n", + "go=1*10**-3 #air gap length(m)\n", + "uo=4*pi*10**-7 #Permeability of free space(A.turns/m)\n", + "ur=1.06*uo #Relativity permeability\n", + "Hc1=-940 #Magnetising force(kA/m)\n", + "Bt=1.25 #Magnetic field induction(T)\n", + "N=1500 #No of turns\n", + "x=3*10**-3 #Position of plunger(m)\n", + "\n", + "#Calculation:\n", + "Ni=-Hc1*d*10**3\n", + "Rx=x/(uo*W1*D)\n", + "Ro=go/(uo*W*D)\n", + "Rm=d/(ur*W*D)\n", + "f=-((Ni)**2)/(uo*W1*D*(Rx+Ro+Rm)**2)\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"The x-directed force:\",round(f,1),\"N\"\n", + "print \"Current in the excitation winding:\",round(Ni/N,2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The x-directed force: -703.3 N\n", + "Current in the excitation winding: 5.01 A\n" + ] + } + ], + "prompt_number": 31 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter4.ipynb b/ELECTRIC_MACHINERY/chapter4.ipynb new file mode 100755 index 00000000..aef0df05 --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter4.ipynb @@ -0,0 +1,277 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 4: Introduction to Rotating Machines" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 4.2, Page number: 199" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "uo=4*pi*10**-7 #Permeabolity of free space(H/m)\n", + "g=0.7*10**-3 #Length of air gap(m)\n", + "p=4 #no. of poles\n", + "Ba=1.6 #Magnetic flux density(T)\n", + "Kr=0.935 #Winding constant\n", + "N=263 #No. of turns\n", + "\n", + "#Calculations:\n", + "Ir=(pi*g*p/(4*uo*Kr*N))*1.6\n", + "\n", + "\n", + "#Results:\n", + "print \"Rotor winding current:\",round(Ir,1),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Rotor winding current: 11.4 A\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 4.3, Page number: 208" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "fc=60 #frequency of the current(Hz)\n", + "p=[2, 4, 6] #matrix of no. of poles\n", + "\n", + "#Calculations:\n", + "ns=[0]*3\n", + "ws=[0]*3\n", + "wc=2*pi*fc\n", + "for n in range(0,3,1):\n", + " ws[n]=round((2/p[n])*wc,0)\n", + " \n", + "for i in range(0,3,1):\n", + " ns[i]=round(120*fc/p[i],0)\n", + "\n", + "\n", + "#Results:\n", + "print \"The synchronous angular velocities:\",ws, \"rad/sec\"\n", + "print \"The speed of the rotor:\",ns,\"r/min\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The synchronous angular velocities: [377.0, 188.0, 126.0] rad/sec\n", + "The speed of the rotor: [3600.0, 1800.0, 1200.0] r/min\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 4.5, Page number: 212" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Nf=68 #Field winding\n", + "Na=18 #Armature winding\n", + "r=0.53 #mean air gap radius(m)\n", + "l=3.8 #Armature winding length(m)\n", + "Kf=0.945 #Winding factor of field winding\n", + "Ka=0.933 #Winding factor of armature winding\n", + "g=4.5*10**-2 #Air gap length(m)\n", + "p=2 #No. of poles\n", + "If=720 #field current(A)\n", + "uo=4*pi*10**-7 #Permeability of free space(H/m)\n", + "f=60 #Frequency curent(Hz)\n", + "\n", + "#Calculations:\n", + "Fag1_peak=4*Kf*Nf*If/(pi*p)\n", + "Bag1_peak=uo*Fag1_peak/g\n", + "Qp=2*Bag1_peak*l*r\n", + "Erms=sqrt(3)*sqrt(2)*pi*f*Ka*Na*Qp\n", + "\n", + "\n", + "#Results:\n", + "print \"The peak fundamental mmf,Fag1_peak: \",round(Fag1_peak/10000,2),\"* 10^4 A.turns/pole\"\n", + "print \"\\nThe flux density in the air gap,Bag1_peak: \",round(Bag1_peak,2),\"T\"\n", + "print \"\\nThe fundamental flux per pole, Qp:\" ,round(Qp,2),\"Wb\"\n", + "print \"\\nThe rms value of open circuit voltage,Erms: \",round(Erms/1000,1),\"KV\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The peak fundamental mmf,Fag1_peak: 2.95 * 10^4 A.turns/pole\n", + "\n", + "The flux density in the air gap,Bag1_peak: 0.82 T\n", + "\n", + "The fundamental flux per pole, Qp: 3.31 Wb\n", + "\n", + "The rms value of open circuit voltage,Erms: 25.7 KV\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 4.8, Page number: 225" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable Declaration:\n", + "ns=1800 #Speed of rotor(rpm)\n", + "f=60 #Frequency(Hz)\n", + "g=1.2*10**-3 #Air gap length(m)\n", + "D=0.27 #Avg diameter of the gap(m)\n", + "Kr=0.976 #Winding factor\n", + "l=0.32 #Axial length(m)\n", + "I=18 #Rotor current(A)\n", + "p=4 #No of poles\n", + "Nr=786 #Rotor windings\n", + "B_max=1.5 #Max. flux densiity(T)\n", + "\n", + "\n", + "#Calculations:\n", + "Fr_max=4*Kr*Nr*I/(pi*p)\n", + "T_max=p*pi*D*l*B_max*Fr_max/4\n", + "wm=ns*pi/30\n", + "P=wm*T_max\n", + "\n", + "\n", + "#Results:\n", + "print \"Maximum torque, T_max:\",round(T_max,0),\"Nm\"\n", + "print \"Maximum power,P:\",round(P/1000,0),\"kW\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Maximum torque, T_max: 1790.0 Nm\n", + "Maximum power,P: 337.0 kW\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 4.9, Page number: 229" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable Declaration:\n", + "b=0.5 #Wavelength of wnding(m)\n", + "l=1.5 #Winding length(m)\n", + "I=700 #Currents in windings(A)\n", + "N=45 #No. of turns\n", + "K=0.92 #winding factor\n", + "p=3 #No. of phases\n", + "uo=4*pi*10**-7\n", + "g=0.01 #Air gap flux(m)\n", + "f=25 #Frequency of the exciting current(A)\n", + "\n", + "#Calculations:\n", + "F_peak=(3*4*K*N*700)/round(4*pi*p,-1)\n", + "B=uo*F_peak/g\n", + "v=f*b\n", + "\n", + "#Results:\n", + "print \"Amplitude of the resultant mmf wave:\",round(F_peak/1000,1),\"kA/m\"\n", + "print \"Peak air gap flux:\",round(B,1),\"T\"\n", + "print \"Velocity of the travelling wave:\",v,\"m/s\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Amplitude of the resultant mmf wave: 8.7 kA/m\n", + "Peak air gap flux: 1.1 T\n", + "Velocity of the travelling wave: 12.5 m/s\n" + ] + } + ], + "prompt_number": 5 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter5.ipynb b/ELECTRIC_MACHINERY/chapter5.ipynb new file mode 100755 index 00000000..d0c5898a --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter5.ipynb @@ -0,0 +1,609 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 5: Synchronous Machines" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.1, Page number: 254" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "import cmath\n", + "\n", + "#Varaible Declaration:\n", + "pf=0.95 #Lagging power factor\n", + "Vl=460 #Terminal voltage(V)\n", + "I=120 #Terminal current(A)\n", + "If=47 #Field current(A)\n", + "X=1.68j #Line syncchronous reactance(ohm)\n", + "\n", + "\n", + "#Calculation:\n", + "#Choosing motor reference direction:\n", + "Va=Vl/math.sqrt(3)\n", + "theta=math.acos(0.95)\n", + "Ia=I*cmath.exp(-theta*1j)\n", + "Eaf=Va-X*Ia\n", + "wc=120*math.pi\n", + "Laf=math.sqrt(2)*abs(Eaf)/(wc*If)\n", + "P=3*Va*Ia*pf\n", + "\n", + "#Results:\n", + "print \"Generated emf:\",round(abs(Eaf),1),\"V line to line\"\n", + "print \"Fied to armature mutual inductance:\",round(Laf*1000,1),\"mH\"\n", + "print \"Three phase power:\",round(abs(P/1000),1),\"kW or\",round(abs(P)/746),\"hp\"\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Generated emf: 278.8 V line to line\n", + "Fied to armature mutual inductance: 22.3 mH\n", + "Three phase power: 90.8 kW or 122.0 hp\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.2, Page number: 255" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath \n", + "from math import *\n", + "\n", + "#Variable Declaration:\n", + "Pin=90.6*10**3 #Input power(kW)\n", + "Va=265.6 #Terninal voltage(V)\n", + "X=1.68j #Synchronous reactance(ohm)\n", + "Laf=22.3*10**-3 #Mutual inductance(H)\n", + "wc=120*pi #Angular frequency(rad/sec)\n", + "\n", + "\n", + "#Calculations:\n", + "Ia=Pin/(3*Va)\n", + "Eaf=Va-X*Ia\n", + "delta=degrees(cmath.phase(Eaf))\n", + "I=sqrt(2)*Eaf/(wc*Laf)\n", + "\n", + "\n", + "#Results:\n", + "print\"The phase angle,delta:\",round(delta,1),\"degrees\"\n", + "print\"Required field current:\",round(abs(I),2),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The phase angle,delta: -35.7 degrees\n", + "Required field current: 55.04 A\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.3, Page number: 257" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Eafl=13.8*10**3 #Open circuit voltage(V)\n", + "If1=318 #Field current(A)\n", + "If2=263 #Field current after extrapolation(A)\n", + "wc=120*pi #Angular frequency(Hz)\n", + "\n", + "#Calculations:\n", + "Eaf=Eafl/sqrt(3)\n", + "La1=sqrt(2)*Eaf/(wc*If1)\n", + "La2=sqrt(2)*Eaf/(wc*If2)\n", + "\n", + "#Results:\n", + "print \"Saturated Laf1:\",round(La1*1000,0),\"mH\" \n", + "print \"Unsaturated Laf1:\",round(La2*1000,0),\"mH\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Saturated Laf1: 94.0 mH\n", + "Unsaturated Laf1: 114.0 mH\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.4, Page number: 262" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Ia=[118, 152] #Armature current from SC Characteristics(A)\n", + "If=[2.20, 2.84] #Field current from SC Characteristics(A)\n", + "Vll=220 #Line-to-line Voltage(V)\n", + "V=202 #Line-to-line air voltage(V) \n", + "P=45*10**3 #Power roted to motor(W) \n", + "Is_sc=1 #per unit rated current(A)\n", + "\n", + "#Calculations:\n", + "Va_ag=V/sqrt(3) #At field current of 2.20A,at air gap,(V)\n", + "Ia_ag=Ia[0]\n", + "Xs_u=Va_ag/Ia_ag\n", + "Ia_rated=P/(sqrt(3)*Vll)\n", + "Xa_g=Va_ag/1\n", + "Xs_u_pu=Va_ag/Is_sc\n", + "Xs=Vll/(Ia[1]*sqrt(3))\n", + "Ia_pu=Ia[1]/Ia[0]\n", + "SCR=If[1]/If[0]\n", + "Xs=1/SCR\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"'All quantities are in per unit values'\"\n", + "print\"Unsaturated value of synchronous reactance:\",round(Xs_u,3),\"ohm\"\n", + "print \"Satureted value of synchronous reactance: \",round(Xs,3),\"ohm\"\n", + "print\"Short circuit ratio:\",round(SCR,3)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "'All quantities are in per unit values'\n", + "Unsaturated value of synchronous reactance: 0.988 ohm\n", + "Satureted value of synchronous reactance: 0.775 ohm\n", + "Short circuit ratio: 1.291\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.5, Page number: 265" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "P_rated=45*10**3 #Rated power(KV)\n", + "Pl=1.80*10**3 #Short circuit load loss(W)\n", + "Ia_pu=1 #Per unit armature current\n", + "Ia=118 #rated armature current(A)\n", + "Ra_dc=0.0335 #Dc resistance(ohm/phase)\n", + "\n", + "\n", + "#Calculations:\n", + "Pl_pu=Pl/P_rated \n", + "Ra_eff1=Pl_pu/Ia_pu**2 #in per unit basis\n", + "Ra_eff2=Pl/(3*(Ia)**2)\n", + "\n", + "#Results:\n", + "print \"Armature resistance in per unit:\",round(Ra_eff1,3),\"per unit\" \n", + "print \"Armature resistance in ohms/phase:\", round(Ra_eff2,3),\"ohms/phase\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Armature resistance in per unit: 0.04 per unit\n", + "Armature resistance in ohms/phase: 0.043 ohms/phase\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.6, Page number: 269" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "import cmath\n", + "import math\n", + "\n", + "\n", + "#Variable declaration:\n", + "Veq=1.0 #Externalsupply(p.u) \n", + "Eaf=1.0 #Internal voltage(p.u)\n", + "Xeq=0.23 #Eqv.resistance of external system(p.u)\n", + "Xs=1.35 #Saturated synchronous reactance(p.u)\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "P_max=Eaf*Veq/(Xs+Xeq)\n", + "\n", + "\n", + "#for part (b):\n", + "delta=[0]*500\n", + "Ia=[0]*500\n", + "Va=[0]*500\n", + "degree=[0]*500\n", + "for n in range(1,101,1):\n", + " delta[n-1]=(pi/2)*(n-1)/100\n", + " Ia[n-1] = (Eaf *exp(1j*delta[n-1]) - Veq)/(1j*(Xs + Xeq))\n", + " Va[n-1] = abs(Veq + 1j*Xeq*Ia[n-1])\n", + " degree[n-1]=180*delta[n-1]/pi\n", + "plot(degree,Va,'r.')\n", + "xlabel('Power angle,delta(degrees)')\n", + "ylabel('Terminal voltage(per unit)')\n", + "title('Terminal voltage vs. power angle for part (b)')\n", + "show()\n", + "#for part (c):\n", + "Vterm=1.0\n", + "P=[0]*500\n", + "deltat=[0]*500\n", + "Ia=[0]*500\n", + "Eaf=[0]*500\n", + "\n", + "for n in range(1,101,1):\n", + " P[n-1]=(n-1)/100\n", + " deltat[n-1]=math.asin(P[n-1]*Xeq/(Vterm*Veq))\n", + " Ia[n-1]=(Vterm*exp(1j*deltat[n-1])-Veq)/(1j*Xeq)\n", + " Eaf[n-1]=abs(Vterm+1j*(Xs+Xeq)*Ia[n-1])\n", + "plot(P,Eaf,'r.')\n", + "xlabel('Power [per unit]')\n", + "ylabel('Eaf [per unit]')\n", + "title('Eaf vs. power for part (c)')\n", + "show()\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"(a) Maximum power supplied to external system:\",round(P_max,2),\"p.u\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['prod', 'Circle', 'power', 'diag', 'sinh', 'trunc', 'binomial', 'plot', 'eye', 'det', 'tan', 'product', 'roots', 'vectorize', 'sin', 'plotting', 'zeros', 'cosh', 'conjugate', 'linalg', 'take', 'solve', 'trace', 'beta', 'draw_if_interactive', 'random', 'ones', 'transpose', 'cos', 'interactive', 'diff', 'invert', 'tanh', 'Polygon', 'reshape', 'sqrt', 'floor', 'source', 'add', 'multinomial', 'test', 'poly', 'mod', 'sign', 'fft', 'gamma', 'log', 'var', 'info', 'seterr', 'flatten', 'nan', 'pi', 'exp']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEZCAYAAACXRVJOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na/B03gAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x3794dd0>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "<matplotlib.figure.Figure at 0x3965f10>" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Maximum power supplied to external system: 0.63 p.u\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.7, Page number: 272" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "P_rated=2000*746/3 #per phase rated power of motor(W)\n", + "Xsm=1.95 #Synchronous reactance(ohm)\n", + "Vl=2300 #Line to line voltage(V)\n", + "f=60 #Angular frequency(Hz)\n", + "p=30 #No. of poles\n", + "Xsg=2.65 #Synchronous reactance of generator(ohm)\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "Vp=2300/sqrt(3)\n", + "Ip=P_rated/Vp\n", + "Eafm=sqrt(Vp**2+(Ip*Xsm)**2)\n", + "Pm=3*Vp*Eafm/Xsm #Max power delivered to motor(W)\n", + "ws=2*2*pi*f/p\n", + "Tmax=Pm/ws #MAx torque of motor(Nm)\n", + "\n", + "\n", + "#for part (b):\n", + "Eafg=sqrt(Vp**2+(Ip*Xsg)**2)\n", + "Pm2=3*Eafm*Eafg/(Xsg+Xsm) #Max power delivered to motor(W)\n", + "Tmax2=Pm2/ws #Max torque(Nm)\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print\"(a) Max power :\",round(Pm/1000,0),\"kW,3-ph\"\n", + "print\" Max torque :\",round(Tmax/1000,1),\"kNm\"\n", + "print \"(b) Max power :\", round(Pm2/1000,0),\"kW,3-ph\"\n", + "print \" Max torque:\", round(Tmax2/1000,1),\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Max power : 3096.0 kW,3-ph\n", + " Max torque : 123.2 kNm\n", + "(b) Max power : 1639.0 kW,3-ph\n", + " Max torque: 65.2 Nm\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.8, Page number: 279" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "\n", + "#Variable declaration:\n", + "P=45 #Power rated(KVA)\n", + "Va=220 #Terminal voltage(V)\n", + "Pin=45 #Power input to the armature(KVA)\n", + "If=5.50 #field current(A)\n", + "Rf=35.5 #Field winding resistance(ohm)\n", + "Ra=0.0399 #Armature dc resistance(ohm/phase)\n", + "Xal=0.215 #Leakage reactance of motor(ohm)\n", + "pf=0.80 #Lagging power factor \n", + "Pc=1.8 #Core loss(kW)\n", + "Pw=0.91 #Friction & windage losses(kW)\n", + "Ps=0.37 #Stray load loss(kW)\n", + "\n", + "\n", + "#Calculations:\n", + "Ia=P*10**3/(sqrt(3)*Va)\n", + "P1=If**2*Rf/10**3 #Loss in field winding(kW)\n", + "P2=3*Ia**2*Ra/10**3 #Loss in armature(kW)\n", + "Pl=(Pc+Pw+Ps+P1+P2)\n", + "Pi=Pin*pf+P1\n", + "Po=Pi-Pl\n", + "eff=(Po/Pi)*100\n", + "\n", + "#Results:\n", + "print \"Efficiency of the synchronous machine:\",round(eff,1),\"%\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Efficiency of the synchronous machine: 84.3 %\n" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.9, Page number: 287" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "import cmath\n", + "\n", + "#Variable declaration:\n", + "Xd=1 #Direct axis synchronus reactance(p.u)\n", + "Xq=0.60 #Quadrature axis synchronous reactance(p.u)\n", + "Va=1 #Terminal voltage(p.u)\n", + "pf=0.8 #Lagging power factor\n", + "Ia=0.8-1j*math.sin(math.acos(0.8)) #Line current(p.u)\n", + "\n", + "\n", + "#Calculations:\n", + "phy=-math.acos(pf)\n", + "E=Va+1j*Xq*Ia\n", + "delta=cmath.phase(E)\n", + "Id=abs(Ia)*math.sin(delta-phy)*cmath.exp(1j*(-pi/2+delta))\n", + "Iq=abs(Ia)*math.cos(delta-phy)*cmath.exp(1j*delta)\n", + "Eaf=Va+Xd*Id*1j+Xq*Iq*1j\n", + "\n", + "\n", + "#Results:\n", + "print \"Generated voltage:\",round(abs(Eaf),2),\"p.u Volt\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Generated voltage: 1.78 p.u Volt\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 5.11, Page number: 291" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from pylab import *\n", + "import cmath\n", + "from sympy import *\n", + "\n", + "#Variable declaration:\n", + "P_rated=2000*746 #Rated power of motor(W)\n", + "Xs=1.95 #Synchronous reactance(ohm/phase)\n", + "Xd=1.95 #Direct axis synchronous reactance(ohm/ph)\n", + "Xq=1.40 #Quadrature axis synchronous reactance(ohm/ph)\n", + "pf=1 #Power factor of the machine\n", + "Vl=2300 #Line to line voltage(V)\n", + "\n", + "#Calculatons:\n", + "Va=float(Vl/sqrt(3)) #volt\n", + "Ia=float(P_rated/(Va*3)) #ampere\n", + "E1=Va-1j*Ia*Xq #From phasor diagram\n", + "delta=cmath.phase(E1) #power angle\n", + "Id=Ia*sin(abs(delta)) #direct axis current(A)\n", + "Eaf=abs(E1)+Id*(Xd-Xq)\n", + "r=symbols('r')\n", + "def P(r): #Process for finding maximum power\n", + " return Eaf*Va*sin(r)/Xd + Va**2*(Xd-Xq)*sin(2*r)/(2*Xd*Xq)\n", + "P1=diff(P(r),r)\n", + "#On differentiation,\n", + "#P1 = 1023732.58489791*cos(r) + 355250.305250306*(2*(cos(r))**2-1)\n", + "l = solve(1023732.58489791*cos(r) + 355250.305250306*(2*(cos(r))**2-1),r)\n", + "P_max = (P(round(l[0],5)))\n", + "\n", + "\n", + "#Results:\n", + "print \"Maximum mechanical power:\",math.ceil(3*P_max/10**3),\"kW,3-phase\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Maximum mechanical power: 3236.0 kW,3-phase\n" + ] + } + ], + "prompt_number": 1 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter6.ipynb b/ELECTRIC_MACHINERY/chapter6.ipynb new file mode 100755 index 00000000..7cd637cd --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter6.ipynb @@ -0,0 +1,540 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 6: Polyphase Induction Machines" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.1, Page number: 318" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "n=3502 #Speed of motor(rpm)\n", + "Pin=15.7 #Input power(kW)\n", + "Ia=22.6 #Terminal current(A)\n", + "R=0.2 #Stator winding resistance(ohm/ph)\n", + "f=60 #frequency(Hz)\n", + "p=2 #No. of poles\n", + "\n", + "#Calculations:\n", + "Ps=3*Ia**2*R/10**3 #Power dissipated in stator winding(kW)\n", + "Pg=Pin-Ps #Air-gap power(kW)\n", + "ns=120*f/p\n", + "s=(ns-n)/ns\n", + "Pr=s*Pg #Power dissipated in stator(kW)\n", + "\n", + "\n", + "#Results:\n", + "print \"Power dissipated in stator:\",round(Pr*10**3,0),\"W\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Power dissipated in stator: 419.0 W\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.2, Page number: 320" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "import math\n", + "\n", + "\n", + "#Variable declaration:\n", + "R1=0.294 #Resistance of stator(ohm)\n", + "R2=0.144 #Rotor resistance referred to stator(ohm)\n", + "X1=0.503 #Reactance of stator(ohm)\n", + "X2=0.209 #Reactance of rotor referred to stator(ohm)\n", + "Xm=13.25 #Leakage reactance(ohm)\n", + "s=0.02 #slip\n", + "Prot=403 #Friction, windage and core losses(W)\n", + "V=220 #Line-to-line voltage(V) \n", + "p=6 #No. of poles\n", + "fc=60 #frequency(Hz)\n", + "nph=3 #No. of phase\n", + "\n", + "#Calculations:\n", + "Zf=((R2/s+1j*X2)*1j*Xm)/(R2/s+1j*X2+1j*Xm)\n", + "Zin=R1+1j*X1+Zf\n", + "V1=V/math.sqrt(3)\n", + "I1=V1/Zin\n", + "a=cmath.phase(I1)\n", + "pf=math.cos(a)\n", + "ns=120*fc/p\n", + "ws=4*math.pi*fc/p\n", + "n=(1-s)*ns\n", + "wm=(1-s)*ws\n", + "Pg=nph*abs(I1)**2*(Zf.real)\n", + "Psh=(1-s)*Pg-Prot\n", + "Tsh=Psh/wm\n", + "Pin=nph*(V1*I1).real\n", + "eff=Psh/Pin\n", + "\n", + "\n", + "#Results:\n", + "print \"Rotor speed: \",n,\"rpm\"\n", + "print \"Output torque: \",round(Tsh,2),\"Nm\"\n", + "print \"Output power: \",round(Psh,2),\"W\"\n", + "print \"Stator current: \",round(abs(I1),1),\"A\"\n", + "print \"Power factor: \",round(pf,3),\"lagging\"\n", + "print \"Efficiency of motor:\",round(eff*100,0),\"%\"\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Rotor speed: 1176.0 rpm\n", + "Output torque: 42.4 Nm\n", + "Output power: 5221.6 W\n", + "Stator current: 18.8 A\n", + "Power factor: 0.846 lagging\n", + "Efficiency of motor: 86.0 %\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.3, Page number: 325" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "from math import *\n", + "\n", + "\n", + "#Variable declaration:\n", + "R1=0.294 #Resistance of stator(ohm)\n", + "R2=0.144 #Rotor resistance referred to stator(ohm)\n", + "X1=0.503 #Reactance of stator(ohm)\n", + "X2=0.209 #Reactance of rotor referred to stator(ohm)\n", + "Xm=13.25 #Leakage reactance(ohm)\n", + "s=0.03 #slip\n", + "V=220 #Line-to-line voltage(V) \n", + "p=6 #No. of poles\n", + "fc=60 #frequency(Hz)\n", + "nph=3 #No. of phase\n", + "\n", + "\n", + "#Calculations:\n", + "#for part (a):\n", + "Zf=((R2/s+1j*X2)*1j*Xm)/(R2/s+1j*X2+1j*Xm) #Impedance referred to stator(ohm) \n", + "Zin=R1+1j*X1+Zf #Total input impedance(ohm)\n", + "Z1_eq=1j*Xm*(R1+1j*X1)/(R1+1j*(X1+Xm)) #Total equiv. impedance(ohm)\n", + "R1_eq=Z1_eq.real\n", + "X1_eq=Z1_eq.imag\n", + "V1=V/sqrt(3)\n", + "V1_eq=V1*(1j*Xm/(R1+1j*(X1+Xm)))\n", + "I2=abs(V1_eq)/sqrt((R1_eq+R2/s)**2+(X1_eq+X2)**2)\n", + "ws=4*pi*fc/p\n", + "ns=120*fc/p\n", + "Tmech=nph*I2**2*(R2/s)/ws\n", + "Pmech=nph*round(I2,1)**2*(R2/s)*(1-s)\n", + "\n", + "\n", + "#for part (b):\n", + "SmaxT=R2/sqrt(R1_eq**2+(X1_eq+X2)**2) #slip at max torque\n", + "n_max=(1-SmaxT)*ns\n", + "Tmax=(1/ws)*(0.5*nph*abs(V1_eq)**2)/(R1_eq+sqrt(R1_eq**2+(X1_eq+X2)**2))\n", + "\n", + "#for part (c):\n", + "s1=1 #Slip at starting of motor\n", + "I2_start=abs(V1_eq)/sqrt((R1_eq+R2)**2+(X1_eq+X2)**2)\n", + "Tstart=nph*I2_start**2*R2/ws\n", + "\n", + "\n", + "#Results:\n", + "print \"(a) Load component I2 of stator current:\",round(I2,1),\"A\"\n", + "print \" Electromechanical torque, Tmech :\",round(Tmech,1),\"Nm\"\n", + "print \" Electromechanical power, Pmech :\",round(Pmech,0),\"W\"\n", + "\n", + "print \"(b) Maximum electromechanical torque :\",round(Tmax,0),\"Nm\"\n", + "print \" Speed :\",round(n_max,0),\"rpm\"\n", + "\n", + "print \"(c) Electromechanical starting torque Tstart:\",round(Tstart,1),\"Nm\"\n", + "print \" Stator load current, I2_start :\",round(I2_start,0),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Load component I2 of stator current: 23.9 A\n", + " Electromechanical torque, Tmech : 65.4 Nm\n", + " Electromechanical power, Pmech : 7979.0 W\n", + "(b) Maximum electromechanical torque : 175.0 Nm\n", + " Speed : 970.0 rpm\n", + "(c) Electromechanical starting torque Tstart: 77.6 Nm\n", + " Stator load current, I2_start : 150.0 A\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.4, Page number: 328" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "import cmath\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "V=230 #line to line voltage(V)\n", + "R1=0.095 #Resistance of stator(ohm)\n", + "X1=0.680 #Reactance of stator(ohm)\n", + "X2=0.672 #Reactance of rotor referred to stator(ohm)\n", + "Xm=18.7 #Leakage reactance(ohm)\n", + "f=60 #frequency(Hz)\n", + "p=4 #No. of poles\n", + "nph=3 #No. of phases\n", + "\n", + "\n", + "#Calculations and Results:\n", + "V1=V/sqrt(3)\n", + "omega=4*pi*f/p\n", + "ns=120*f/p\n", + "Z1eq=1j*Xm*(R1+1j*X1)/(R1+1j*(X1+Xm)) #Stator thevenin equivalent\n", + "R1eq=Z1eq.real\n", + "X1eq=Z1eq.imag\n", + "V1eq=abs(V1*1j*Xm/(R1+1j*(X1+Xm)))\n", + "\n", + "print \"Hence, the required plot is shown below:\"\n", + "for m in range(1,6,1): #Loop over rotor resistance\n", + " if m==1:\n", + " R2=0.1\n", + " elif m==2:\n", + " R2=0.2\n", + " elif m==3:\n", + " R2=0.5\n", + " elif m==4:\n", + " R2=1.0\n", + " else:\n", + " R2=1.5\n", + "\n", + " s=[0]*202\n", + " rpm=[0]*202\n", + " Tmech=[0]*202\n", + " for n in range(1,201,1): #Loop over slip\n", + " s[n-1]=n/200 #slip\n", + " rpm[n-1]=ns*(1-s[n-1]) #rpm\n", + " I2=abs(V1eq/(Z1eq+1j*X2+R2/s[n-1])) #I2\n", + " Tmech[n-1]=nph*I2**2*R2/(s[n-1]*omega) #Electromechanical torque(Nm)\n", + "\n", + " plot(rpm,Tmech)\n", + " title('Electromechanical mechanical torque, Tmech(Nm) vs rpm')\n", + " xlabel(\"rpm\")\n", + " ylabel(\"Tmech\")\n", + " if m==1:\n", + " show()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "Hence, the required plot is shown below:\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['f']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEZCAYAAABrUHmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npDcmDSIi0huTBhER6e3/AR/6J3Y/Fed8AAAAAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x1dff550>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEZCAYAAABrUHmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "<matplotlib.figure.Figure at 0x2c9aad0>" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.5, Page number: 335" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "from sympy import *\n", + "import math\n", + "\n", + "#Variable declaration:\n", + "nph=3 #No. of phases\n", + "k=0.429 #reactance ratio(X1/X2) from table 6.1,for class C motor\n", + "p=4 #No.of poles\n", + "#Test 1: No-load test at 60 Hz\n", + "V1=219 #Applied voltage, line-to-lne(V)\n", + "I1_nl=5.70 #Phase current(A)\n", + "Pnl=380 #Power(W)\n", + "ft=60 #Hz\n", + "\n", + "#Test 2: Blocked-rotor test at 15 Hz\n", + "V2=26.5 #Applie voltage, line-to-line(V)\n", + "I1_bl=18.57 #Phase current(A)\n", + "Pbl=675 #Power(W)\n", + "fbl=15 #Hz\n", + "\n", + "#Test 3:\n", + "R1=0.262 #Avg resistance per stator phase(ohm)\n", + "\n", + "#Test 4: Blocked-rotor test at 60 Hz\n", + "V4=212 #Applied voltage, line-line (V)\n", + "I2_bl=83.3 #Avg phase current(A)\n", + "Pbl_4=20.1*10**3 #Power(W)\n", + "Tstart=74.2 ##starting torque(Nm)\n", + "\n", + "\n", + "#Calculations:\n", + "#For part (a):\n", + "Prot=Pnl-nph*I1_nl**2*R1\n", + "V1_nl=V1/sqrt(3) #from test 1\n", + "Qnl=sqrt((nph*V1_nl*I1_nl)**2-Pnl**2)\n", + "Xnl=Qnl/(nph*I1_nl**2)\n", + "V1_bl=V2/sqrt(3) #from test 2\n", + "Qbl=sqrt((nph*V1_bl*I1_bl)**2-Pbl**2)\n", + "Xbl=(ft/fbl)*(Qbl/(nph*I1_bl**2))\n", + "X2=symbols('X2')\n", + "fx=k**2*X2**2+(Xbl*(1-k)-Xnl*(1+k))*X2+Xnl*Xbl\n", + "x=solve(fx,X2)\n", + "X2=round(x[0],2) #since X2 must be less than X1\n", + "X1=k*X2\n", + "Xm=Xnl-X1\n", + "Rbl=Pbl/(nph*I1_bl**2)\n", + "R2=(Rbl-R1)*((X2+Xm)/Xm)**2\n", + "\n", + "#for part (b):\n", + "Pg=Pbl_4-nph*I2_bl**2*R1\n", + "ws=4*math.pi*ft/p\n", + "Tstart=Pg/ws\n", + "\n", + "\n", + "#Results:\n", + "print \"(a) N-load rotational loss:\",round(Prot,0),\"W\"\n", + "print \"\\n Equivalent ckt parameters:\\n\"\n", + "print\" R1=\",round(R1,3),\"ohm\",\" R2=\",round(R2,3),\"ohm\"\n", + "print\" X1=\",round(X1,3),\"ohm\",\" X2=\",round(X2,3),\"ohm\",\" Xm=\",round(Xm,2),\"ohm\"\n", + "print \"\\n(b) Starting torque:\",round(Tstart,2),\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) N-load rotational loss: 354.0 W\n", + "\n", + " Equivalent ckt parameters:\n", + "\n", + " R1= 0.262 ohm R2= 0.447 ohm\n", + " X1= 0.635 ohm X2= 1.48 ohm Xm= 21.2 ohm\n", + "\n", + "(b) Starting torque: 77.7 Nm\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.6, Page number: 338" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Xnl=21.8 #ohm\n", + "Xbl=2.01 #ohm\n", + "R_1=0.26 #ohm\n", + "Rbl=0.65 #ohm\n", + "V=220 #volt\n", + "#Here are the two sets of parameters\n", + "#Set 1 corresponds to the exact solution\n", + "#Set 2 corresponds to the approximate solution\n", + "\n", + "R1=[0.262, 0.262] #ohm\n", + "R2=[0.447, 0.444] #ohm\n", + "X1=[0.633, 0.603] #H\n", + "X2=[1.47, 1.41] #H\n", + "Xm=[21.2, 21.2] #H\n", + "nph=3 #No. of phases\n", + "p=4 #No. of poles\n", + "Prot=354 #Rotational losses(Watts)\n", + "\n", + "#Calculations:\n", + "X_1=0.3*Xbl #(ohm) from table 6.1 and X1+X2=Xbl\n", + "X_2=Xbl-X_1 #ohm\n", + "X_m=Xnl-X_1\n", + "R_2=(Rbl-R_1)*((X_2+X_m)/X_m)**2\n", + "\n", + "#Results for part (a):\n", + "print \"(a) The parameters:\\n\"\n", + "print\" R1=\",round(R_1,3),\"ohm\",\" R2=\",round(R_2,3),\"ohm\"\n", + "print\" X1=\",round(X_1,3),\"ohm\",\" X2=\",round(X_2,2),\"ohm\"\n", + "print\" Xm=\",round(X_m,3),\"ohm\"\n", + "\n", + "#Calculations & Results for part (b):\n", + "print \"\\n\\n(b)\"\n", + "#Here is the operating condition\n", + "V1=220/sqrt(3)\n", + "fe=60 #Hz\n", + "rpm=1746\n", + "#Calculate the synchronous speed:\n", + "ns=120*fe/p\n", + "ws=4*pi*fe/p\n", + "s=(ns-rpm)/ns\n", + "wm=ws*(1-s)\n", + "Zgap=[0]*2\n", + "Zin=[0]*2\n", + "Pmech=[0]*2\n", + "I1=[0]*2\n", + "I2=[0]*2\n", + "Tmech=[0]*2\n", + "\n", + "#Calculate stator Thevenin equivalent:\n", + "#Loop over the two motors\n", + "for m in range(0,2,1):\n", + " Zgap = 1j*Xm[m]*(1j*X2[m] + R2[m]/s)/(R2[m]/s + 1j*(Xm[m] + X2[m]))\n", + " Zin=R1[m]+1j*X1[m]+Zgap\n", + " I1=V1/Zin\n", + " I2=I1*(1j*Xm[m])/(R2[m]/s+1j*(Xm[m]+X2[m]))\n", + " Tmech=nph*abs(I2)**2*R2[m]/(s*ws) #Electromechanical torque\n", + " Pmech=wm*Tmech #Electromechanical power\n", + " Pshaft=Pmech - Prot\n", + " if (m==0):\n", + " print \"Exact Solution:\"\n", + " else:\n", + " print \"\\nApproximate Solution:\"\n", + "\n", + "\n", + "\n", + " \n", + " print \"\\tPmech=\",round(Pmech,1),\"W\",\"\\tPshaft =\",round(Pshaft,1), \"W\"\n", + " print \"\\tI1=\", round(abs(I1),1),\"A\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) The parameters:\n", + "\n", + " R1= 0.26 ohm R2= 0.443 ohm\n", + " X1= 0.603 ohm X2= 1.41 ohm\n", + " Xm= 21.197 ohm\n", + "\n", + "\n", + "(b)\n", + "Exact Solution:\n", + "\tPmech= 2820.7 W \tPshaft = 2466.7 W\n", + "\tI1= 10.3 A\n", + "\n", + "Approximate Solution:\n", + "\tPmech= 2850.5 W \tPshaft = 2496.5 W\n", + "\tI1= 10.4 A\n" + ] + } + ], + "prompt_number": 7 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter7.ipynb b/ELECTRIC_MACHINERY/chapter7.ipynb new file mode 100755 index 00000000..c5e8eebf --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter7.ipynb @@ -0,0 +1,473 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 7: DC Machines" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.1, Page number: 371" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Vt=[128, 124] #Terminal voltage(V)\n", + "Ea=125 #Generated emf(V)\n", + "Ra=0.02 #Armature resistance(ohm)\n", + "n=3000 #rpm\n", + "\n", + "\n", + "#Calculations:\n", + "#For 128 V\n", + "Ia1=(Vt[0]-Ea)/Ra\n", + "Pin1=Vt[0]*Ia1\n", + "Pe1=Ea*Ia1\n", + "wm=3000*2*pi/60\n", + "Tmech1=Ea*Ia1/wm\n", + "\n", + "#for 124 V\n", + "Ia2=(-Vt[1]+Ea)/Ra\n", + "Pin2=Vt[1]*Ia2\n", + "Pe2=Ea*Ia2\n", + "Tmech2=Ea*Ia2/wm\n", + "\n", + "\n", + "\n", + "#Results:\n", + "print \"(a) Armature current:\",Ia1,\"A\",\"\\n Terminal power:\",Pin1/10**3,\"kW\"\n", + "print \" Electromagnetic power:\",round(Pe1/10**3,2),\"kW\"\n", + "print \" Torque:\",round(Tmech1,1),\"Nm\"\n", + "\n", + "print \"(b) Armature current:\",Ia2,\"A\",\"\\n Terminal power:\",Pin2/10**3,\"kW\"\n", + "print \" Electromagnetic power:\",round(Pe2/10**3,2),\"kW\",\n", + "print \"\\n Torque:\",round(Tmech2,1),\"Nm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Armature current: 150.0 A \n", + " Terminal power: 19.2 kW\n", + " Electromagnetic power: 18.75 kW\n", + " Torque: 59.7 Nm\n", + "(b) Armature current: 50.0 A \n", + " Terminal power: 6.2 kW\n", + " Electromagnetic power: 6.25 kW \n", + " Torque: 19.9 Nm\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.2, Page number: 372" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Vt=123 #terminal voltage(V)\n", + "Pt=21.9 #Terminal power(kW)\n", + "Ra=0.02 #ohm\n", + "Eao=125 #generated voltage(V) at 3000rpm\n", + "no=3000 #rpm\n", + "\n", + "\n", + "#calculations:\n", + "Ia=Pt*10**3/Vt\n", + "Ea=Vt-Ia*Ra\n", + "n=(Ea/Eao)*no\n", + "\n", + "#Results:\n", + "print \"Speed of motor:\",round(n,0),\"rpm\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Speed of motor: 2867.0 rpm\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.3, Page number: 376" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Il=400 #Armature current(A)\n", + "If=4.7 #Field current(A)\n", + "Ns=3 #series turns per pole\n", + "Nf=1000 #shunt field turns per pole\n", + "Eao=274 #at Ia=0,(V)\n", + "n=1150 #speed of motor(rpm)\n", + "no=1200 #rated speed(rpm) \n", + "Ra=0.025 #armature resistance(ohm)\n", + "Rs=0.005 #series field resistance(ohm)\n", + "\n", + "\n", + "#Calculations:\n", + "Is=Il+If\n", + "GM=If+(Ns/Nf)*Is #for graphical analysis\n", + "Ea=(n/no)*Eao\n", + "Vt=Ea-Is*(Ra+Rs)\n", + "\n", + "#Results:\n", + "print \"Terminal voltage at rated terminal current:\",round(Vt,0),\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Terminal voltage at rated terminal current: 250.0 V\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.4, Page number: 377" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Il=400 #Armature current(A)\n", + "If=4.7 #Field current(A)\n", + "Ns=3 #series turns per pole\n", + "Nf=1000 #shunt field turns per pole\n", + "Eao=261 #at Ia=400 A,(V)\n", + "n=1150 #speed of motor(rpm)\n", + "no=1200 #rated speed(rpm) \n", + "Ra=0.025 #armature resistance(ohm)\n", + "Rs=0.005 #series field resistance(ohm)\n", + "\n", + "\n", + "#Calculations:\n", + "Ea=(n/no)*Eao\n", + "Vt=Ea-(Il+If)*(Ra+Rs)\n", + "\n", + "#Results:\n", + "print \"Terminal voltage:\", round(Vt,0), \"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Terminal voltage: 238.0 V\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.5, Page number: 378" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "\n", + "#Variable declaration:\n", + "Il=400 #Armature current(A)\n", + "If=4.7 #Field current(A)\n", + "Ns=3 #series turns per pole\n", + "Nf=1000 #shunt field turns per pole\n", + "Eao=269 #at Ia=400 A,(V)\n", + "n=1150 #speed of motor(rpm)\n", + "no=1200 #rated speed(rpm) \n", + "Ra=0.025 #armature resistance(ohm)\n", + "Rs=0.007 #series field resistance(ohm)\n", + "\n", + "#Calculations:\n", + "Is=Il+If\n", + "GM=If+(Ns/Nf)*Is #for graphical analysis\n", + "Ea=(n/no)*Eao\n", + "Vt=Ea-Is*(Ra+Rs)\n", + "\n", + "#Results:\n", + "print \"Terminal voltage at rated terminal current:\",round(Vt,0),\"V\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Terminal voltage at rated terminal current: 245.0 V\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.6, Page number: 381" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "\n", + "\n", + "#Variable declaration:\n", + "Ns=4 #Series field turns\n", + "Nf=1000 #Shunt field turns\n", + "Vt=250 #Full load voltage(V)\n", + "#for part (a):\n", + "Ia=400 #Armature current(A)\n", + "Ra=0.025 #Armature resistance(ohm)\n", + "\n", + "#for part (b):\n", + "Rs=0.005 #Added sries resistance(ohm)\n", + "Vo=250 #No load voltage(V)\n", + "If=5 #field current at full load(A)\n", + "\n", + "\n", + "#Calculations & Results:\n", + "\n", + "#for part (a)\n", + "V1=Ia*Ra\n", + "\n", + "#for part (b):\n", + "Ia1=Ia+If\n", + "Rs,Rd=symbols('Rs Rd') #Rd= diverter resistance(ohm)\n", + "Rp=Rs*Rd/(Rs+Rd) # -------(i)\n", + "Is=Ia1*(Rd/(Rs+Rd))\n", + "Inet=If+(Ns/Nf)*Is\n", + "Ea=Vt+Ia*(Ra+Rp) # -------(ii)\n", + "\n", + "#from equation (ii)\n", + "Rp=Rs(Inet-5.0)/1.62 \n", + "R_d=0.0082 #R_d=Rd(say), using (i)\n", + "print \"(a) The operating terminal voltage = 205 V\", Inet\n", + "print \"(b) Rd =\", R_d,\"ohm\"\n", + "print \"\\tHence, by this process, resistance across the series field\" \n", + "print \"\\t(referred to as a series-field diverter) can be adjusted \"\n", + "print \"\\tto produce the desired performance. \"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) The operating terminal voltage = 205 V 1.62*Rd/(Rd + Rs) + 5\n", + "(b) Rd = 0.0082 ohm\n", + "\tHence, by this process, resistance across the series field\n", + "\t(referred to as a series-field diverter) can be adjusted \n", + "\tto produce the desired performance. \n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.7, Page number: 383" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + " \n", + "#Variable declaration:\n", + "Ia=400 #Armature current(A)\n", + "n1=1200 #rpm\n", + "n2=1100 #rpm\n", + "Ra=0.025 #armature resistance(ohm) \n", + "Eo=250 #no load armature voltage(V)\n", + "del_n=1.5 #fractional winding added\n", + "N=1000 #Total windings\n", + "\n", + "\n", + "#Calculations:\n", + "#for part(a):\n", + "#point corresponding on the no load saturation curve is :\n", + "Eao=Eo*(n1/n2)\n", + "#using Eao value in curve, value of If is found to be:\n", + "If=5.90 #Field current(A)\n", + "Ea=Eo-Ia*Ra\n", + "#From Fig. 7.14\n", + "Ea1=261\n", + "n=n1*(Ea/Ea1)\n", + "Pe=Ea*Ia\n", + "Pl=2000 #No load Rotational loss(W)\n", + "Po=(Pe-Pl)/(1+0.01)\n", + "\n", + "#for part (b):\n", + "If1=If+del_n/N\n", + "#From Fig. 7.14 the corresponding value of Ea at 1200 r/min would be 271 V.\n", + "Ea2=271 #volts\n", + "n22=n1*(Ea/Ea2)\n", + "\n", + "\n", + "#Results:\n", + "print \"Part(a):\"\n", + "print \"Required speed =\",round(n),\"r/min\"\n", + "print \"Output power =\", round((Po/746),1),\"hp\"\n", + "print \"\\nPart (b):\"\n", + "print \"Required speed =\",round(n22),\"r/min\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Part(a):\n", + "Required speed = 1103.0 r/min\n", + "Output power = 124.8 hp\n", + "\n", + "Part (b):\n", + "Required speed = 1063.0 r/min\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 7.9, Page number: 389" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "V1=50 #terminal voltage(V)\n", + "Ia=1.25 #Armature current(A)\n", + "Ra=1.03 #Armature resistance(ohm)\n", + "n1=2100 #speed at 50V(rpm)\n", + "V2=48 #terminal voltage at 1700 rpm (V)\n", + "n2=1700 #speed at 48 V(rpm)\n", + "\n", + "\n", + "\n", + "#Calculations:\n", + "#for (a):\n", + "Ea1=V1-Ia*Ra\n", + "wm1=n1*2*pi/60\n", + "Km=round(Ea1/wm1,2)\n", + "\n", + "#for part(b):\n", + "Prot=Ea1*Ia\n", + "\n", + "#for part(c:)\n", + "wm2=n2*2*pi/60\n", + "Ea2=Km*wm2\n", + "Ia2=(V2-Ea2)/Ra\n", + "Pmech=Ea2*Ia2\n", + "Pshaft=Pmech-Prot\n", + "\n", + "#Results:\n", + "print \"(a) Torque constant:\",round(Km,2),\"V/(rad/s)\"\n", + "print \"(b) No-load rotational losses of the motor:\",round(Prot,0),\"W\"\n", + "print \"(c) The power output of the motor:\",round(Pshaft,2),\"W\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Torque constant: 0.22 V/(rad/s)\n", + "(b) No-load rotational losses of the motor: 61.0 W\n", + "(c) The power output of the motor: 275.05 W\n" + ] + } + ], + "prompt_number": 3 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter8.ipynb b/ELECTRIC_MACHINERY/chapter8.ipynb new file mode 100755 index 00000000..1a4aef36 --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter8.ipynb @@ -0,0 +1,427 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 8: Variable-Reluctance Machines and Stepping Motors" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 8.1, Page number: 411" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from numpy import *\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "R=0.038 #m\n", + "a=b=pi/3 #rad\n", + "g=2.54*10**-4 #m\n", + "D=0.13 #m\n", + "N=100 #turns in both poles\n", + "uo=4*pi*10**-7 #permeability of free space()\n", + "i1=5 #coil current (A)\n", + "\n", + "\n", + "#Calculation:\n", + "Lm=N**2*uo*a*R*D/(2*g)\n", + "#x=symbols('x')\n", + "subplot(2,1,1)\n", + "x=linspace(-180,-120,100)\n", + "L=-(Lm/60)*x-2*Lm\n", + "plot(x,L,'b')\n", + "#grid()\n", + "\n", + "x=linspace(-60,0,100)\n", + "L=(Lm/60)*x+Lm\n", + "plot(x,L,'b')\n", + "grid()\n", + "\n", + "x=linspace(0,60,100)\n", + "L=-(Lm/60)*x+Lm\n", + "plot(x,L,'b')\n", + "grid()\n", + "\n", + "\n", + "x=linspace(120,180,100)\n", + "L=(Lm/60)*x-2*Lm\n", + "plot(x,L)\n", + "annotate('Lm=0.128 H',xy=(-150,0.10))\n", + "annotate('Lmax',xy=(0,Lm+0.005))\n", + "ylabel('L11(theta)')\n", + "xlabel('theta')\n", + "grid()\n", + "\n", + "#part(b)\n", + "subplot(2,1,2)\n", + "x1=linspace(-180,-120,100)\n", + "x2=linspace(-150,-90,100)\n", + "i1=5\n", + "i2=4\n", + "Tm1=(Lm/(2*pi/3))*i1**2\n", + "Tm2=(Lm/(2*pi/3))*i2**2\n", + "dll=np.ones(100)\n", + "plot(x1,-Tm1*np.array(dll),'g')\n", + "plot(x2,Tm2*np.array(dll),'b--')\n", + "\n", + "x1=linspace(-60,0,100)\n", + "x2=linspace(-90,-30,100)\n", + "Tm1=(Lm/(2*pi/3))*i1**2\n", + "Tm2=(Lm/(2*pi/3))*i2**2\n", + "dll=np.ones(100)\n", + "plot(x1,Tm1*np.array(dll),'g')\n", + "plot(x2,-Tm2*np.array(dll),'b--')\n", + "\n", + "x1=linspace(0,60,100)\n", + "x2=linspace(30,90,100)\n", + "Tm1=(Lm/(2*pi/3))*i1**2\n", + "Tm2=(Lm/(2*pi/3))*i2**2\n", + "dll=np.ones(100)\n", + "plot(x1,-Tm1*np.array(dll),'g')\n", + "plot(x2,Tm2*np.array(dll),'b--')\n", + "\n", + "x1=linspace(120,180,100)\n", + "x2=linspace(90,150,100)\n", + "Tm1=(Lm/(2*pi/3))*i1**2\n", + "Tm2=(Lm/(2*pi/3))*i2**2\n", + "dll=np.ones(100)\n", + "plot(x1,Tm1*np.array(dll),'g')\n", + "plot(x2,-Tm2*np.array(dll),'b--')\n", + "grid()\n", + "ylim(-3,3)\n", + "annotate('___ i1=I1, i2=0', xy=(110,2.6))\n", + "annotate('---- i1=0, i2=I2', xy=(110,2.2))\n", + "ylabel('Torque [N.m]')\n", + "xlabel('thetam [degrees]')\n", + "\n", + "#Results:\n", + "print \"Lm =\",Lm,\"H\"\n", + "print \"(c)The peak torque =\",round(Tm1,2),\"N.m\"\n", + "print \"\\t(i) The net torque, (at thetam=0) =\", 0, \"N.m\"\n", + "print \"\\t(ii) The net torque, (at thetam=45 deg.) =\", 0, \"N.m\"\n", + "print \"\\t(iii)The net torque, (at thetam=75 deg) =\", round(Tm1,2), \"N.m\"\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "Lm =" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 0.127968099059 H\n", + "(c)The peak torque = 1.53 N.m\n", + "\t(i) The net torque, (at thetam=0) = 0 N.m\n", + "\t(ii) The net torque, (at thetam=45 deg.) = 0 N.m\n", + "\t(iii)The net torque, (at thetam=75 deg) = 1.53 N.m\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEPCAYAAABV6CMBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIL0ocIiLiixKHiIj4osQhIiK+/ADYIdwwaPRWXQAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x1d13bd0>" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 8.3, Page number: 424" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "from sympy import *\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "n1=4000 #r/min\n", + "R=0.038 #m\n", + "a=b=pi/3 #rad\n", + "g=2.54*10**-4 #m\n", + "D=0.13 #m\n", + "N=100 #turns in both poles\n", + "uo=4*pi*10**-7 #permeability of free space(H/m)\n", + "Ll=0.005 #H\n", + "Vo=100 #phase voltage applied to phase 1.(V)\n", + "\n", + "\n", + "#Calculation:\n", + "wm=n1*pi/30\n", + "Lm=N**2*uo*a*R*D/(2*g)\n", + "thetam=symbols('thetam')\n", + "t=symbols('t')\n", + "#for part (a):\n", + "#for -60<=thetam<=0deg,\n", + "L11=Ll+(Lm/(pi/3))*(thetam+pi/3)\n", + "L111=diff(L11,thetam)\n", + "R1=L111*wm\n", + "#which is nuch greater than resistance R=1.5 ohm\n", + "thetam=-pi/3+wm*t\n", + "i1=Vo*t/(float(round(Ll,3))+float(Lm/(pi/3))*thetam+float(Lm/(pi/3))*pi/3)\n", + "\n", + "#for part (b):\n", + "V2=-200 #applied voltage(V)\n", + "thetam2=symbols('thetam2')\n", + "L12=Ll+(Lm/(pi/3))*(pi/3-thetam2)\n", + "L112=diff(L12,thetam2)\n", + "to=2.5*10**-3 #ms\n", + "thetam2=float(-pi/3+wm*to)\n", + "i1=Vo*t/(float(round(Ll,3))+float(Lm/(pi/3))*thetam+float(Lm/(pi/3))*pi/3)\n", + "i2=(0.25-200*(t-to))/(0.005+51.1*(5*10**-3-t))\n", + "\n", + "\n", + "#Results:\n", + "print \"i1 =\",i1,\"\\t, (where round(16.2934044186179*pi,2) = 51.1 )\"\n", + "print \"\\ni2 =\",i2,\"\\n\"\n", + "\n", + "\n", + "#Calculations & Results:\n", + "#for part (c):\n", + "from __future__ import division\n", + "from pylab import *\n", + "\n", + "Lleak=0.005\n", + "Posintegral=0\n", + "integral=0\n", + "N1=500\n", + "tmax=3.75*10**-3\n", + "t=[0]*503\n", + "thet=[0]*503\n", + "Torque=[0]*503\n", + "deltat = tmax/N1\n", + "thetm=[0]*503\n", + "i=[0]*503\n", + "for n in range(1,N1+2,1):\n", + " t[n-1]=tmax*(n-1)/N1\n", + " thetm[n-1]=-(pi/3)+(400*pi/3)*t[n-1]\n", + " if (thetm[n-1]<=0):\n", + " i[n-1]=100*t[n-1]/(0.005+51.1*t[n-1])\n", + " dld1d1theta = 0.122\n", + " Torque[n-1]=0.5*i[n-1]**2*dld1d1theta\n", + " Posintegral=Posintegral+Torque[n-1]*deltat\n", + " integral=Posintegral\n", + " else:\n", + " i[n-1]=(0.25-200*(t[n-1]-2.5*10**-3))/(0.005+51.1*(5*10**-3-t[n-1]))\n", + " dld11dtheta = -0.122\n", + " Torque[n-1] = 0.5*i[n-1]**2*dld11dtheta\n", + " integral = integral + Torque[n-1]*deltat\n", + "\n", + "print \"\\nPositve torque integral =\",Posintegral, \"[N-m-sec]\"\n", + "print \"\\nTorque integral=\",integral,\"[N-m-sec]\\n\"\n", + "\n", + "plot(1000*np.array(t),i)\n", + "xlabel('time [msec]')\n", + "ylabel('Phase current [A]')\n", + "title('(a) phase-1 current profile')\n", + "grid()\n", + "show()\n", + "plot(1000*np.array(t),Torque)\n", + "xlabel('time [msec]')\n", + "ylabel('Torque [N-m]')\n", + "title('(b) torque profile')\n", + "grid()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "i1 =" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 100*t/(51.1872396234976*t + 0.005) \t, (where round(16.2934044186179*pi,2) = 51.1 )\n", + "\n", + "i2 = (-200*t + 0.75)/(-51.1*t + 0.2605) \n", + "\n", + "\n", + "Positve torque integral = 0.000456384094483 [N-m-sec]\n", + "\n", + "Torque integral= 0.000335463884625 [N-m-sec]\n", + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['fmod', 'cosh', 'sinh', 'trunc', 'tan', 'gamma', 'degrees', 'radians', 'sin', 'expm1', 'ldexp', 'isnan', 'frexp', 'ceil', 'copysign', 'cos', 'tanh', 'fabs', 'sqrt', 'hypot', 'log', 'log10', 'pi', 'log1p', 'floor', 'modf', 'exp', 'isinf', 'e']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x388b510>" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEZCAYAAABrUHmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "<matplotlib.figure.Figure at 0x3a36f90>" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 8.4, Page number: 433" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "\n", + "#Variavle declaration:\n", + "rpm=2500 #rpm of motor\n", + "\n", + "\n", + "#Calculations & Results:\n", + "#For part (a):\n", + "theta=[0]*12\n", + "i=[0]*102\n", + "lambda1=[0]*102\n", + "for m in range(1,11,1):\n", + " theta[m-1]=10*(m-1)\n", + " for n in range(1,102,1):\n", + " i[n-1]=30*(n-1)/100\n", + " lambda1[n-1]=i[n-1]*(0.005+0.09*((90-theta[m-1])/90))*(8/(i[n-1]+8))\n", + "\n", + " \n", + " plot(i,lambda1,'.')\n", + " \n", + " if m==1:\n", + " hold(True)\n", + " \n", + "xlabel('current [A]')\n", + "ylabel('Lambda [Wb]')\n", + "title('Family of lambda-i curves as theta_m varies from 0 to 90 degrees') \n", + "annotate('theta_m=0 deg',xy=(6,0.03))\n", + "annotate('theta_m=0 deg',xy=(8,0.5))\n", + "\n", + "\n", + "#for part (b):\n", + "lambdamax=25*(0.005+0.09*(8/(25+8)))\n", + "AreaWnet=0\n", + "AreaWrec=0\n", + "deli=0.25\n", + "for n in range(1,102,1):\n", + " i[n-1]=25*(n-1)/100\n", + " AreaWnet=AreaWnet + deli*i[n-1]*(0.09)*(8/(i[n-1]+8))\n", + " AreaWrec=AreaWrec + deli*(lambdamax-i[n-1]*(0.005+0.09*(8/(i[n-1]+8))))\n", + "\n", + "Ratio=(AreaWnet+AreaWrec)/AreaWnet\n", + "print \"part (b): Ratio =\", round(Ratio,2)\n", + "\n", + "#for part(b):\n", + "rps=rpm/60\n", + "T=1/rps\n", + "Pphase=2*AreaWnet/T\n", + "Ptot=2*Pphase\n", + "print \"part (c): AreaWnet =\", round(AreaWnet,2),\"Joules\"\n", + "print \"Pphase =\",round(Pphase),\"W\",\"\\tPtot =\",round(Ptot),\"W\\n\"\n", + "plot(AreaWrec=0.7,AreaWnet=25)\n", + "grid()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "part (b): Ratio =" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.55\n", + "part (c): AreaWnet = 9.91 Joules\n", + "Pphase = 825.0 W \tPtot = 1651.0 W\n", + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['power', 'random', 'fft', 'linalg', 'info']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAAEZCAYAAACnyUNvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x3a59f50>" + ] + } + ], + "prompt_number": 4 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/chapter9.ipynb b/ELECTRIC_MACHINERY/chapter9.ipynb new file mode 100755 index 00000000..b9eac00a --- /dev/null +++ b/ELECTRIC_MACHINERY/chapter9.ipynb @@ -0,0 +1,404 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 9: Single- and Two-Phase Motors" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 9.1, Page number: 459" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "from sympy import *\n", + "import math\n", + "\n", + "#Variable declaration:\n", + "Zmain=4.5+3.7j #main winding impedance(ohm)\n", + "Zaux=9.5+3.5j #auxilliary winding impedance(ohm)\n", + "f=60 #frequency(Hz)\n", + "\n", + "\n", + "#Calculations:\n", + "phy_main=math.degrees(math.atan(Zmain.imag/Zmain.real))\n", + "phy=phy_main-90\n", + "w=2*pi*60\n", + "Xc=symbols('Xc')\n", + "a=solve((3.5+Xc)/9.5-math.tan(math.radians(float(phy))), Xc)\n", + "C=-1/(w*a[0])\n", + "\n", + "\n", + "#Results:\n", + "print \"The starting capacitance:\",round(float(C)*10**6,0), \"uF\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The starting capacitance: 176.0 uF\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 9.2, Page number: 467" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import cmath\n", + "from math import *\n", + "\n", + "\n", + "#Variable Declaration:\n", + "R1_m=2.02 #resistance of main winding(ohm)\n", + "X1_m=2.79 #resistance of main\n", + "R2_m= 4.12 #Rotor resistance ref. to stator(ohm)\n", + "X2_m=2.12 #Rotor reactance ref. to stator(ohm)\n", + "Xm=66.8 #Magnetising reactance(ohm)\n", + "s=0.05 #slip\n", + "Pcu=24 #copper loss(W)\n", + "Pw=13 #friction & windage loss(W)\n", + "V=110 #line-to-line voltage(V)\n", + "p=4 #no.of poles\n", + "fc=60 #frequency(Hz)\n", + "\n", + "#Calculations:\n", + "X22=X2_m+Xm\n", + "Q2_m=X22/R2_m\n", + "Rf=(Xm**2/X22)*(1/(s*Q2_m+1/(s*Q2_m)))\n", + "Xf=(X2_m*Xm/X22)+Rf/(s*Q2_m)\n", + "Zf=Rf+1j*Xf #forward field impedance(ohm)\n", + "\n", + "Rb=R2_m*(Xm/X22)**2/(2-s)\n", + "Xb=(X2_m*Xm/X22)+Rb/((2-s)*Q2_m)\n", + "Zb=Rb+1j*Xb #bachward field impedance\n", + "Zt=0.5*(Zf+Zb)+R1_m+1j*X1_m\n", + "I=V/abs(Zt) #Stator current(A)\n", + "pf=cos(cmath.phase(Zt)) #power factor\n", + "Pin=V*I*pf\n", + "Pg_f=I**2*0.5*Rf #power absorbed by forward field(W)\n", + "Pg_b=I**2*0.5*Rb #power absorbed by backward field(W)\n", + "Pmech=(1-s)*(Pg_f-Pg_b)\n", + "Pshaft=Pmech-(Pcu+Pw)\n", + "ws=(2/p)*120*pi\n", + "ns=(120/p)*fc\n", + "n=(1-s)*ns #Rotor speed(rpm)\n", + "wm=(1-s)*ws\n", + "Tshaft=Pshaft/wm #shaft torque(Nm)\n", + "eff=Pshaft/Pin\n", + "\n", + "#Results:\n", + "print \"Stator current:\",round(I),\"A\", \"\\nPower factor:\",round(pf,3)\n", + "print \"Power output:\",round(Pshaft),\"W\", \"\\nSpeed:\",n,\"rpm\"\n", + "print \"Shaft torque:\",round(Tshaft,3),\"Nm\",\"Efficiency\",round(eff*100),\"%\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Stator current: 4.0 A \n", + "Power factor: 0.621\n", + "Power output: 147.0 W \n", + "Speed: 1710.0 rpm\n", + "Shaft torque: 0.823 Nm Efficiency 60.0 %\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 9.3, Page number: 474" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "import math\n", + "import cmath\n", + "\n", + "\n", + "#Variable declaration:\n", + "f=60 #freq(Hz)\n", + "omeag=2*pi*f\n", + "s=0.05 #slip\n", + "R1=0.534 #resistance of main winding(ohm)\n", + "X1=2.45\n", + "Xm=70.1\n", + "R2=0.956\n", + "X2=2.96\n", + "Valpha=230\n", + "Vbeta=210*cmath.exp(1j*80*pi/180)\n", + "\n", + "#Calculations:\n", + "Vf = 0.5*(Valpha - 1j*Vbeta)\n", + "Vb = 0.5*(Valpha + 1j*Vbeta)\n", + "Zf=R1+1j*X1+1j*Xm*(R2/s+1j*X2)/(R2/s+1j*(X2+Xm))\n", + "If=Vf/Zf\n", + "Zb=R1+1j*X1+1j*Xm*(R2/(2-s)+1j*X2)/(R2/(2-s)+1j*(X2+Xm))\n", + "Ib = Vb/Zb\n", + "Ialpha=If+Ib\n", + "Ibeta=1j*(If-Ib)\n", + "Pgf=2*((Vf*(If.conjugate())).real-R1*abs(If)**2)\n", + "Pgb=2*((Vb*(Ib.conjugate())).real-R1*abs(Ib)**2)\n", + "Pmech=(1-s)*(Pgf-Pgb)\n", + "\n", + "\n", + "#Results:\n", + "print \"(a) Positive seq components:\", round(Vf.real,1)+1j*round(Vf.imag,1),\"V\"\n", + "print\" Negative seq. components:\", round(Vb.real,1)+1j*round(Vb.imag,1),\"V\"\n", + "\n", + "print\"\\n(b) Positive stator currents:\",round(If.real,1)+1j*round(If.imag,1),\"A\"\n", + "print\" Negative stator currnets:\",round(Ib.real,1)+1j*round(Ib.imag,1),\"A\"\n", + "\n", + "print\"\\n(c) Positive currents:\",round(Ialpha.real,1)+1j*round(Ialpha.imag,1),\"A\"\n", + "print\" Negative currnets:\",round(Ibeta.real,1)+1j*round(Ibeta.imag,1),\"A\"\n", + "\n", + "print \"\\n(d) Power to forward field:\",round(Pgf,0),\"W\"\n", + "print \" Power to backward field:\",round(Pgb,0),\"W\"\n", + "print \" Pmech:\",round(Pmech,0),\"W\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(a) Positive seq components: (218.4-18.2j) V\n", + " Negative seq. components: (11.6+18.2j) V\n", + "\n", + "(b) Positive stator currents: (9.3-6.3j) A\n", + " Negative stator currnets: (3.7-1.5j) A\n", + "\n", + "(c) Positive currents: (13-7.8j) A\n", + " Negative currnets: (4.8+5.6j) A\n", + "\n", + "(d) Power to forward field: 4149.0 W\n", + " Power to backward field: 15.0 W\n", + " Pmech: 3928.0 W\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 9.5, Page number: 483" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from __future__ import division\n", + "%pylab inline\n", + "import cmath\n", + "from math import *\n", + "\n", + "#Variable declaration:\n", + "Lmain=0.0806 #main winding inductance(H)\n", + "Rmain = 0.58 #main winding resistance(ohm)\n", + "Laux = 0.196 #auxilliary winding inductance(H)\n", + "Raux = 3.37 #auxilliary winding resistance(ohm)\n", + "Lr=4.7*10**-6 #rotor inductance(H)\n", + "Rr=37.6*10**-6 #rotor resistance(ohm)\n", + "Lmain_r=0.588*10**-3 #main inductance ref. to rotor(H)\n", + "Laux_r = 0.909*10**-3 #aux inductance ref. to rotor(H)\n", + "p=2 #poles\n", + "Vo=230 #terminal voltage(V)\n", + "w=120*pi #angular frequency(Hz)\n", + "C=35*10**-6\n", + "Prot=40 #Windage losses(W)\n", + "Pcore=105 #Core loss(W)\n", + "n=3500 #rpm\n", + "\n", + "\n", + "#calculations and Results:\n", + "Xc=-1/(w*C)\n", + "speed=[0]*102\n", + "for cal in range(1,3,1):\n", + " if cal==1:\n", + " mmax=2\n", + " else:\n", + " mmax=102\n", + " for m in range(1,mmax,2):\n", + " if cal==1:\n", + " speed[m-1]=3500\n", + " else:\n", + " speed[m-1]=3599*(m-1)/100\n", + " \n", + " ns=(2/p)*3600\n", + " s=(ns-speed[m-1])/ns\n", + "\n", + "#for part (a):\n", + " Kplus=s*w/(2*(Rr+1j*s*w*Lr))\n", + " Kminus=(2-s)*w/(2*(Rr+1j*(2-s)*w*Lr))\n", + " A1=Lmain-1j*Lmain_r**2*(Kplus+Kminus)\n", + " A2=Lmain_r*Laux_r*(Kplus-Kminus)\n", + " A3=Laux-1j*Laux_r**2*(Kplus+Kminus)\n", + " M=[[0]*2,[0]*2]\n", + " M[0][0]=Rmain + 1j*w*A1\n", + " M[0][1] = 1j*w*A2;\n", + " M[1][0] = -1j*w*A2;\n", + " M[1][1] = Raux + 1j*Xc+ 1j*w*A3\n", + " V=[[Vo],[-Vo]]\n", + " M1=inv(M)\n", + " I=dot(M1,V)\n", + " Imain=I[0][0]\n", + " Iaux=I[1][0]\n", + " Is=Imain-Iaux\n", + " magImain=abs(Imain)\n", + " angleImain=math.degrees(cmath.phase(Imain))\n", + " magIaux=abs (Iaux)\n", + " angleIaux=math.degrees(cmath.phase(Iaux))\n", + " magIs=abs(Is)\n", + " angleIs=math.degrees(cmath.phase(Is))\n", + " Vcap=Iaux*Xc\n", + " magVcap=abs(Vcap)\n", + " \n", + " #for part (b):\n", + " Tmech=[0]*102\n", + " Pshaft=[0]*102\n", + " Tmechl = (Kplus-Kminus).conjugate()\n", + " Tmechl=Tmechl*(Lmain_r**2*Imain*((Imain).conjugate())+Laux_r**2*Iaux*((Iaux).conjugate()))\n", + " Tmech2 = 1j*Lmain_r*Laux_r*((Kplus+Kminus).conjugate())\n", + " Tmech2 = Tmech2*((Imain).conjugate()*Iaux-Imain*((Iaux).conjugate()));\n", + " Tmech[m-1] = (p/2)*(Tmechl+Tmech2).real\n", + " Pshaft=((2/p)*(1-s)*w*Tmech[m-1])-Prot\n", + " \n", + " #for part (c):\n", + " Pmech=[0]*102\n", + " Pmain = (Vo*(Imain.conjugate())).real\n", + " Paux = (-Vo*(Iaux.conjugate())).real\n", + " Pin = Pmain+Paux+Pcore\n", + " eta = Pshaft/Pin;\n", + " if cal==1:\n", + " print \"part (a):\"\n", + " print \"\\nImain=\",round(magImain,1),\"A at an angle\",round(angleImain,1),\"degrees\"\n", + " print \"\\nImain=\",round(magIaux,1),\"A at an angle\",round(angleIaux,1),\"degrees\"\n", + " print \"\\nImain=\",round(magIs,1),\"A at an angle\",round(angleIs,1),\"degrees\"\n", + " print \"\\nVcap=\",round(magVcap,0),\"V\"\n", + " print \"\\npart (b):\"\n", + " print \"\\nTmech=\",round(Tmech[0],2),\"Nm\"\n", + " print \"\\nPshaft=\",round(Pshaft),\"W\"\n", + " print \"\\npart (c):\"\n", + " print \"\\nPmain=\",round(Pmain,0),\"W\"\n", + " print \"\\nPaux=\",round(Paux,0),\"W\"\n", + " print \"\\nPin=\",round(Pin,0),\"W\"\n", + " print \"\\nEfficiency=\",round(eta*100,1),\"%\"\n", + " else:\n", + " \n", + " plot(speed,Tmech,'g.')\n", + " xlabel('speed (rpm)')\n", + " ylabel('Tmech (Nm)')\n", + " title('Electromagnetic torque vs speed')\n", + " show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "part (a):" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "\n", + "Imain= 15.9 A at an angle -37.6 degrees\n", + "\n", + "Imain= 5.2 A at an angle -150.8 degrees\n", + "\n", + "Imain= 18.5 A at an angle -22.7 degrees\n", + "\n", + "Vcap= 394.0 V\n", + "\n", + "part (b):\n", + "\n", + "Tmech= 9.75 Nm\n", + "\n", + "Pshaft= 3532.0 W\n", + "\n", + "part (c):\n", + "\n", + "Pmain= 2893.0 W\n", + "\n", + "Paux= 1043.0 W\n", + "\n", + "Pin= 4041.0 W\n", + "\n", + "Efficiency= 87.4 %\n" + ] + }, + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "WARNING: pylab import has clobbered these variables: ['Polygon', 'poly', 'sign', 'ldexp', 'hypot', 'flatten', 'conjugate', 'diff', 'tan', 'Circle', 'roots', 'plot', 'isnan', 'eye', 'trace', 'fabs', 'floor', 'diag', 'invert', 'nan', 'modf', 'sqrt', 'frexp', 'source', 'add', 'degrees', 'take', 'var', 'zeros', 'pi', 'log10', 'plotting', 'product', 'seterr', 'power', 'multinomial', 'copysign', 'transpose', 'expm1', 'ceil', 'test', 'beta', 'ones', 'isinf', 'sinh', 'vectorize', 'cosh', 'mod', 'cos', 'prod', 'e', 'f', 'tanh', 'det', 'radians', 'sin', 'binomial', 'solve', 'log', 'fmod', 'reshape', 'exp', 'trunc', 'log1p', 'gamma', 'interactive']\n", + "`%pylab --no-import-all` prevents importing * from pylab and numpy\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEZCAYAAAB8culNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICIiBSYHIiJSYHIgIiKF/w/ondA2j7ZbKwAAAABJRU5ErkJggg==\n", + "text": [ + "<matplotlib.figure.Figure at 0x2ab5110>" + ] + } + ], + "prompt_number": 5 + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file diff --git a/ELECTRIC_MACHINERY/screenshots/capture1.png b/ELECTRIC_MACHINERY/screenshots/capture1.png Binary files differnew file mode 100755 index 00000000..1196a40b --- /dev/null +++ b/ELECTRIC_MACHINERY/screenshots/capture1.png diff --git a/ELECTRIC_MACHINERY/screenshots/capture2.png b/ELECTRIC_MACHINERY/screenshots/capture2.png Binary files differnew file mode 100755 index 00000000..56b278d5 --- /dev/null +++ b/ELECTRIC_MACHINERY/screenshots/capture2.png diff --git a/ELECTRIC_MACHINERY/screenshots/capture3.png b/ELECTRIC_MACHINERY/screenshots/capture3.png Binary files differnew file mode 100755 index 00000000..a8df3c9e --- /dev/null +++ b/ELECTRIC_MACHINERY/screenshots/capture3.png |
