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author | Thomas Stephen Lee | 2015-09-04 22:04:10 +0530 |
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committer | Thomas Stephen Lee | 2015-09-04 22:04:10 +0530 |
commit | 41f1f72e9502f5c3de6ca16b303803dfcf1df594 (patch) | |
tree | f4bf726a3e3ce5d7d9ee3781cbacfe3116115a2c /ELECTRIC_MACHINERY_by_Fitzgerald_Kingsley_and_Umans/chapter8.ipynb | |
parent | 9c9779ba21b9bedde88e1e8216f9e3b4f8650b0e (diff) | |
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diff --git a/ELECTRIC_MACHINERY_by_Fitzgerald_Kingsley_and_Umans/chapter8.ipynb b/ELECTRIC_MACHINERY_by_Fitzgerald_Kingsley_and_Umans/chapter8.ipynb new file mode 100755 index 00000000..785bba7e --- /dev/null +++ b/ELECTRIC_MACHINERY_by_Fitzgerald_Kingsley_and_Umans/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", + "%matplotlib 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": 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ZrVbViy++iNWrV8M53/9teU/2xhtv4Pr166V+UrEoseEAgMhIwN0dmDFD6iRl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\nrl69inr16tl8HB7jYIwxcclijMMUMRq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+ "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", + "%matplotlib 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": 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cS9cJbpGRkZw9e7bCO3e3nBxITISuXY05vlWubbZCTitkBMnpapXN+Yc/QEGB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\ntWoxZMgQNmzYUGy7ioQ2e1e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+ "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", + "%matplotlib 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": 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arSaffvqpYN+uu+46sn//fkIIIc888wxJSkrq8ZzywQ6pTZvmmRCaN8M2LkJh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\naNR1qM4+wHTq0MnJCb6+vgCA6dOnw9/fH7dv3zaJOtRkG2A69afY9PD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