symbolic dynamic models for highly varying power system loads

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102 Phase B EAF current 6 4 CT Current (A) 2 0 -2 1 515 1029 1543 2057 2571 3085 3599 4113 4627 5141 5655 6169 6683 7197 7711 8225 8739 9253 9767 -4 -6 time (Time is shown in ‘points’. One point = 0.1 ms) Figure C.2 A sample of EAF current <strong>for</strong> phase B Phase C EAF current 6 4 CT current (A) 2 0 -2 -4 1 515 1029 1543 2057 2571 3085 3599 4113 4627 5141 5655 6169 6683 7197 7711 8225 8739 9253 9767 -6 -8 time (Time is shown in ‘points’. One point = 0.1 ms) Figure C.3 A sample of EAF current <strong>for</strong> phase C
103 138 kV BUS 100 MVA 138/34.5 kV 30 MVA 138/34.5 kV 3i 2000/5 3v 175/1 34.5 kV BUS 34.5 kV BUS 3i 2000/5 3i 2000/5 2000/5 1i EAF Figure C.4 Electric arc furnace schematic
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ARIZONA STATE UNIVERSITY POWER SYST
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include statistical tests, electric
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CHAPTER Page 2.6 Forecasting a sign
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LIST OF TABLES Table Page 3.1 Test
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LIST OF FIGURES Figure Page 2.1 Rep
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G n H 0 H 1 IM IO I pred IREQ J KS
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1 CHAPTER 1 1.1 Motivation INTRODUC
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3 Static load model: Most of the lo
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5 the voltage response of the load
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7 In 1999-2000, Dinesh at Arizona S
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9 increasing or decreasing the valu
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11 initial conditions, which genera
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13 equipment. It simply identifies
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15 Fractional occurrence: Fractiona
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17 consecutive symbols at a time, a
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19 CSI = ∑( f χ i * fγi) /( f
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21 To check the accuracy of the abo
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CHAPTER 3 SYNTHETIC TESTS AND RESUL
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25 3.4 Analysis tools To analyze th
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27 The KS method involves identifyi
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29 3.5 Test results ST-A01: The aim
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31 ST-B02: Here the Matlab inline f
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33 Table 3.5 Results for tests in c
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35 Table 3.6 Results for category D
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37 Table 3.7 Results for category E
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39 CHAPTER 4 TESTS ON INDUSTRIAL AR
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41 2. Constant number of cells: The
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43 Table 4.2 Test statistics for di
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45 Table 4.5 Results for RT-D03 Ind
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47 was recorded with the same sampl
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49 RT-E02: In this test maximum wor
Page 63 and 64: 51 RT-E04: To implement the propose
Page 65 and 66: 53 4.5 Discussion Load modeling: Fo
Page 67 and 68: 55 Table 4.11 Execution time compar
Page 69 and 70: 57 • Implementing a probabilistic
Page 71 and 72: 59 integration of physical informat
Page 73 and 74: 61 [1] M. S. Chen, “Determining l
Page 75: 63 [18] R. B. Fancher, A. E. Hulse,
Page 79 and 80: 67 A.1 Dictionary Formation In this
Page 81 and 82: 69 data sets and compares them by c
Page 83 and 84: 71 c=zeros(w,1); c(1:w)=x(s:s+w-1);
Page 85 and 86: 73 kount1(r1)=kount1(r1)+1; kount1(
Page 87 and 88: 75 y1=sin(x1*pi/12.5)+(1/3)*sin(3*x
Page 89 and 90: 77 if (r>=cwf(i))&(r
Page 91 and 92: 79 % A program to forecast a signal
Page 93 and 94: 81 f=f-1; end rr=rr+1; end r=r+1; e
Page 95 and 96: 83 for jj=1:f if(d(jj,2:nw)==0) min
Page 97 and 98: 85 A.5 Forecasting a signal using M
Page 99 and 100: 87 end f=f+1; d(f,1:w)=(c(1:w))'; e
Page 101 and 102: 89 if(d(j,i+1)~=0) if(x(nx)==d(j,i)
Page 103 and 104: 91 end end newwd; wdlen; mm=wdlen+1
Page 105: 93 APPENDIX B ILLUSTRATIVE EXAMPLE
Page 108 and 109: 96 Z can be defined as Z(i) = Z(i+1
Page 110 and 111: 98 For a given s, the table entry i
Page 112 and 113: 100
Page 116: 104 Table C.1 Sample data sheet for
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symbolic dynamic models for highly varying power system loads

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