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Diffstat (limited to 'ELECTRIC_MACHINERY/.ipynb_checkpoints')
4 files changed, 1906 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": 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ibCiGqVKvAU4DsP/GfszznydsKIbxuYNUw2K96m0CQpzAsLS0REFBASwsLJCf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\nVVOFQzcPiRPmf3gTYFh9T5t62+9tLOi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+ "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": 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ExEQAQGJiIsLDw7UZRoum3oVkZmLGAWci0poGtRCKiopgZmaGNm3aALh9nYSS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\nIjY2VnTt2lV4eHiIqKgocfP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XyFeDl5cXmjZtiitXrqB9+/bYs2cP/Pz8yuxz/fp1rqj3/2JjYxEbG2vqZpgF9sVj7IvH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\np07QaDQAgKVLl2L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+ "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": 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0J06fPo0NGzaYfZ0gCHjppZcQGxsLANDr9UhNTTXOh279l0NGRgb07+hx9Z9X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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": 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\ 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": 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PPkBoaCjCwsIAAK+//joqKysBABkZGdizZw+ystqbw3K5HDk5OUZJQYScxBzehZWIyER2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\nYII=\n", 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dqL29Z9qL01xEQ4fL353CCTExMXbbYmNjnXmqQ+fOnRP33nuviI2NFSkpKeL8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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": 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TjD6djPYxrzB7Zmu8z0pFSloKklOTUalsJbSo1QItarVAa+PW6GDaAW3rt0XlciU4wPS+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\ngQFWOK/Ic16Ivyl1FOgHGBYWhrCwMOV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Z/fnnn0REtHnzZhozZgwREbm7u9MPP/yQp/7T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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": 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PYffu3Wjbtq1y5M/x48cxadKk2ldIRET1ktZAePXVVxEfHw8HBwcAQKdOnbB/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\n11RvA2H37t0aRyktXrxY5s2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+s2+K7OxsBAcHIzAwEOvXr2+zjFarxfjx4xEaGoopU6Z0+AsQEZF5KbaCW11d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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/.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": {} + } + ] +}
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