path: root/ELECTRIC_MACHINERY/chapter6.ipynb

{
 "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": {}
  }
 ]
}