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  {
   "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",
      "%matplotlib inline\n",
      "import cmath\n",
      "from math import *\n",
      "from matplotlib import *\n",
      "from pylab 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": [
        "Hence, the required plot is shown below:\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0yrdlC1tnsXVr7c9R9HwWKquzSE5OdrSwsMiZOXPmgTt37nTp1q3b39u2bVss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\n0qktCAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x32859d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZxwPatQM2bwZGjpStLABTFgxQ18wX1dW4VVKCW6WlCCspQblAgF5/KY8+urrw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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x34979d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "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": {}
  }
 ]
}