summaryrefslogtreecommitdiff
path: root/Introduction_to_Electric_Drives_by_J._S._Katre/chapter9.ipynb
blob: 538bde124bf7ade25d10bb5552982e538c3372f9 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
{
 "metadata": {
  "name": "",
  "signature": "sha256:a22e1723285478b613c9e93f05f8b7152fa3f22e73d3c58b27e1a2cccb6de797"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter9, Power factor improvement"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.4.3: page 9-15"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import array, nditer\n",
      "from math import pi, cos\n",
      "from __future__ import division\n",
      "#plot the varaition of average load voltage with firing angle\n",
      "alpha=array([0, 30, 60, 90]) #firing angle in degree\n",
      "ea=array([(2/pi)*cos(pi/180*alpha[0]), (2/pi)*cos(pi/180*alpha[1]), (2/pi)*cos(pi/180*alpha[2]), (2/pi)*cos(pi/180*alpha[3])])\n",
      "#############PLOT###########\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "plt.plot(alpha,ea) #\n",
      "plt.ylabel(\"Average load voltage(in terms of Vm)\")\n",
      "plt.xlabel(\"Firing angle (alpha)\")\n",
      "plt.title(\"Variation of Ea Vs alpha for SAC\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEZCAYAAACXRVJOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wMaKZhDPQoRYAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7912985e50>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.5.1 : page 9-22"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import degrees, acos, sqrt, pi, cos\n",
      "#IS_rms, I1_rms, FPF, PF and HF\n",
      "#given data :\n",
      "Vm=230 # in volts\n",
      "Ia=12 # in A\n",
      "Av=200 # average load voltage in volts\n",
      "alfa = acos(Av*pi/Vm/sqrt(2)-1)*180/pi\n",
      "Is_rms=Ia*sqrt((180-alfa)/180) \n",
      "print  \"(1)for PAC : \"\n",
      "print \"(a) Is_rms = %0.2f A\" %Is_rms\n",
      "I1_rms=((2*sqrt(2))/pi)*Ia*cos(alfa/2*pi/180) \n",
      "print \"(b) I1_rms = %0.2f A\" %I1_rms\n",
      "fi=alfa/2 \n",
      "FPF=cos(pi/180*fi) \n",
      "print \"(c) FPF = %0.4f lag\" %FPF\n",
      "CDF=I1_rms/Is_rms \n",
      "print \"(d) CDF = %0.4f \"%CDF\n",
      "PF=CDF*FPF \n",
      "print \"(e) PF = %0.4f lag\" %PF\n",
      "HF=sqrt((1/CDF**2)-1) \n",
      "print \"(f) HF = %0.4f \"%HF\n",
      "print  \"(2)for SAC : \"\n",
      "Vm=230 # in volts\n",
      "Ia=12 # in A\n",
      "Av=200 # average load voltage in volts\n",
      "alfa = degrees(acos(Av*pi/2/sqrt(2)/Vm))\n",
      "Is_rms=Ia*sqrt((180-(2*alfa))/180) \n",
      "print \"(a) Is_rms = %0.2f A\" %Is_rms\n",
      "I1_rms=((2*sqrt(2))/pi)*Ia*cos(alfa*pi/180) \n",
      "print \"(b) I1_rms = %0.2f A\" %I1_rms\n",
      "CDF=I1_rms/Is_rms \n",
      "print \"(c) CDF = %0.3f \"%CD\n",
      "fi=0 # degree\n",
      "FPF=cos(pi/180*fi) \n",
      "print \"(d) FPF = %0.2f \"%FPF\n",
      "#in book CDF is mentioned as DF which is wrongly mentioned\n",
      "PF=CDF*FPF \n",
      "print \"(e) PF = %0.3f lagging\" %PF\n",
      "HF=(sqrt((1/CDF**2)-1))*100 \n",
      "print \"(f) HF = %0.2f %%\" %HF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1)for PAC : \n",
        "(a) Is_rms = 11.27 A\n",
        "(b) I1_rms = 10.62 A\n",
        "(c) FPF = 0.9828 lag\n",
        "(d) CDF = 0.9423 \n",
        "(e) PF = 0.9261 lag\n",
        "(f) HF = 0.3552 \n",
        "(2)for SAC : \n",
        "(a) Is_rms = 10.95 A\n",
        "(b) I1_rms = 10.43 A\n",
        "(c) CDF = 0.953 \n",
        "(d) FPF = 1.00 \n",
        "(e) PF = 0.953 lagging\n",
        "(f) HF = 31.91 %\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.5.2 : page 9-23"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "from math import sqrt, pi, cos\n",
      "#average voltage \n",
      "a1=30 #in degree\n",
      "a2=75 #in degree\n",
      "b1=60 #in degree\n",
      "ia=10 #in amperes\n",
      "vsrms=230 #in volts\n",
      "b3=180-a1 #\n",
      "a3=180-b1 #\n",
      "b2=180-a2 #\n",
      "alfa=0 #\n",
      "vldc=((vsrms*sqrt(2))/pi)*(cos(pi/180*a1)-cos(pi/180*b1)+cos(pi/180*a2)-cos(pi/180*b2)+cos(pi/180*a3)-cos(pi/180*b3)) \n",
      "print \"average voltage = %0.1f V\" %vldc\n",
      "Is_rms=ia*((1/180)*(b1-a1+b2-a2+b3-a3))**(1/2) #\n",
      "print \"Is_rms = %0.2f A\" %Is_rms\n",
      "I1_rms=((sqrt(2)*ia)/(pi))*(cos(pi/180*a1)-cos(pi/180*b1)+cos(pi/180*a2)-cos(pi/180*b2)+cos(pi/180*a3)-cos(pi/180*b3)) \n",
      "print \"I1_rms = %0.2f A\" %I1_rms\n",
      "fi=alfa \n",
      "FPF=cos(I1_rms*fi) \n",
      "print \"FPF = %0.2f\"%FPF\n",
      "DF=I1_rms/Is_rms \n",
      "print \"DF = %0.4f\"%DF\n",
      "PF=DF*FPF \n",
      "print \"PF = %0.4f lag\" %PF\n",
      "HF=sqrt((1/DF**2)-1) \n",
      "print \"HF = %0.2f %%\" %(HF*100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "average voltage = 129.4 V\n",
        "Is_rms = 7.07 A\n",
        "I1_rms = 5.63 A\n",
        "FPF = 1.00\n",
        "DF = 0.7956\n",
        "PF = 0.7956 lag\n",
        "HF = 76.15 %\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 9.5.3: page 9-24"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#IS_rms, I1_rms, PF and HF\n",
      "#given data :\n",
      "Vm=230 # in volts\n",
      "Ia=10 # in A\n",
      "alpha=pi/6 #degree\n",
      "ea=((2*Vm*sqrt(2))/pi)*cos(alpha) #\n",
      "print \"average output voltage = %0.2f V\" %ea\n",
      "isrms=Ia*(1-(2*alpha)/pi)**(1/2) #\n",
      "print \"rms value of supply current = %0.2f A\" %isrms\n",
      "I1rms=((2*sqrt(2)*Ia*cos(alpha))/pi) #\n",
      "print \"rms value of fundamental component of supply current = %0.2f A\" %I1rms\n",
      "hf=((isrms/I1rms)**2-1)**(1/2) #\n",
      "print \"HF of supply current = %0.2f %%\"%(hf*100)\n",
      "PF=((sqrt(2))*(1+cos(alpha)))/((pi*(pi-alpha))**(1/2)) #\n",
      "print \"PF (lagging)of supply current = %0.2f %%\"%PF\n",
      "# Answer for HF is calculated wrong in the textbook."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "average output voltage = 179.33 V\n",
        "rms value of supply current = 8.16 A\n",
        "rms value of fundamental component of supply current = 7.80 A\n",
        "HF of supply current = 31.08 %\n",
        "PF (lagging)of supply current = 0.92 %\n"
       ]
      }
     ],
     "prompt_number": 42
    }
   ],
   "metadata": {}
  }
 ]
}