performance evaluation of swarm intelligence based power system ...

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13.2. UNIT COMMITMENTSheble et al. [56] have presented a genetic-based unit commitment (UC)scheduling algorithm. It has made use of GA with domain specific mutation operatorsfor finding good unit commitment schedules. The test results of three different electricutilities have been compared with that of Lagrangian relaxation UC method.Bakirtzis et al. [57] have developed a genetic algorithm that uses differentquality function techniques to solve the unit commitment problem. The test results upto 100 generator units have been compared with that of dynamic programming andLagrangian relaxation methods.Swarup et al. [58] have employed a new solution methodology to the UCproblem using genetic algorithm. The strategy has been found to be efficient andserve to handle larger size UC problems.Zwe-Lee Gaing 1591 has built an integrated approach of discrete binaryparticle swarm optimization (BPSO) with the lambda-iteration method for solving theUC problem. It has been solved as two subproblems using BPS0 method forminimization of the transition cost. The economic dispatch problem has been solvedby lambda-iteration method for the minimization of the production cost. Thefeasibility of the method has been demonstrated on a 10- and a 26-unit systems, andthe test results have been compared with that of GA method.Zhao et al. [60] have presented an improved particle swarm optimization(IPSO) algorithm for power system UC problem. It has adopted an orthogonal designin order to generate the initial population that are scattered uniformly over a feasiblesolution space. The IPSO algorithm has been tested on a modeled 10-unit system andthe performance is compared with that of GA and EP methods.Ting et al. [61] have integrated a new approach of hybrid particle swarmoptimization (HPSO) scheme, which is a blend of HPSO, BPSO and real-codedparticle swarm optimization (RCPSO), to solve the UC problem. The UC problem has
een handled by BPSO, whereas the economic load dispatch problem has been solvedby RCPSO.Funabashi et al. [62] have formulated a twofold simulated annealing methodfor the optimization of fuzzy-based UC model. The method has served to offer arobust solution for UC problem.Victoire et al. [63] have applied a hybrid PSO and sequential quadraticprogramming (SQP) technique, prelude to tabu search (TS) method for solving theUC problem. The combinational part of the UC problem has been solved using the TSmethod. The nonlinear optimization part of economic dispatch problem (EDP) hasbeen solved using a hybrid PSO-SQP technique. The effectiveness of hybridoptimization technique has been tested on a NTPS zone-11 7-unit system.1.3.3. REAL POWER LOSS MINIMIZATION AND VOLTAGE STABILITYENHANCEMENTYoshida et al. [64] have applied a PSO technique to reactive poweroptimization problem. The objective has been to find the optimal settings of somecontrol variables that served to minimize the total real power losses in a network. Theproblem has been classified as a mixed-integer nonlinear optimization problem sincesome variables are continuous whereas others are discrete. The test results of anIEEE-14 bus system, a practical 112 bus system and a large scale 1217 bus systemhave been compared with that of reactive tabu search (RTS) and enumerationmethods.Miranda et al. [65] have introduced a hybrid PSO approach to VoltageNArcontrol problem in power systems. They have combined evolutionary strategies withPSO to improve the robustness of the classical PSO. The test results of an IEEE-24bus system have been compared with that of simulated annealing based approach.Mantawy et al. [66] have investigated the same problem using particle swarmoptimization for an IEEE-6 bus system. The simulation results of the PSO algorithmhave been compared with that of previously reported works in the literature.
Page 1 and 2: PERFORMANCE EVALUATION OFSWARM INTE
Page 3 and 4: DECLARATION1 hereby declare that th
Page 5 and 6: In practical systems, the operating
Page 7 and 8: ACKNOWLEDGEMENTI express my deep se
Page 9 and 10: CONTENTSCHAPTERTITLEPAGE No.ABSTRAC
Page 11 and 12: CHAPTER TITLE PAGE No.2.4.4.2. Eval
Page 13 and 14: PACE No.6.3.2. Phase Advance Compen
Page 15 and 16: TABLE No.2.15.2.16.2.17.2.18.2.19.2
Page 17 and 18: xviiTABLE No.TITLEPAGE No.A.6.B.1.B
Page 19 and 20: LIST OF FIGURESFIGURE No. TITLE PAG
Page 21 and 22: LIST OF ABBREVIATIONSPSOParticle Sw
Page 23 and 24: Initial weightFinal weightMaximum i
Page 25 and 26: I0v ISubscript jpdWm*=Balanced load
Page 29 and 30: programming, mixed integer pmgrammi
Page 31 and 32: 1.3.1. ECONOMIC LOAD DISPATCH PROBL
Page 33: Fung et al. [51] have formulated an
Page 37 and 38: 13.4. DISTRIBUTED GENERATION @G) FO
Page 39 and 40: A new topology based on 2-source in
Page 41 and 42: A novel PSO-based approach to quant
Page 43 and 44: economic load dispatch (ELD) for co
Page 45 and 46: B. Constraint Satisfaction Techniqu
Page 47 and 48: itself and the term c,Rand,( ) x (g
Page 49 and 50: the particle's position to pbest po
Page 51 and 52: wlthln the constraints. The details
Page 53 and 54: 7. Modify the member position of ea
Page 55 and 56: By the method of curve fitting, the
Page 57 and 58: Table 2.2. Optimal scbeduliig of ge
Page 59 and 60: 5-5 1 0 1 5 m U m 3 5 . 0 U 5 0Nmnb
Page 61 and 62: Table 2.9. Solution odiffereut meth
Page 63 and 64: where V, (i) and VmX(1) are minimum
Page 65 and 66: 2. Initialize randomly the individu
Page 67 and 68: Table 2.1 1. ELD results obtained h
Page 69 and 70: The computational procedure of pric
Page 71 and 72: Table 2.14. CEED raub - IEEE-30 bus
Page 73 and 74: adjusted instantaneously. Even thou
Page 75 and 76: 2.43. Futures of Genetic Algorithms
Page 77 and 78: Tbe GA's can be distinguished hrn o
Page 79 and 80: 2. To each chromosome of the popula
Page 81 and 82: Table 2.16. Optimal generator dispa
Page 83 and 84: MethodGA method[421PSO method1421Hy
Page 85 and 86:
Fig. 2.1 1 (;A-IDSO contergence cha
Page 87 and 88:
CHAPTER 3INTEGRATED GA-PSO BASED UN
Page 89 and 90:
upon any one of the two possible st
Page 91 and 92:
where Tdom, is the minimum down tim
Page 93 and 94:
2. ... lln are integers ranging h m
Page 95 and 96:
3. For each chromosome economic loa
Page 97:
Tnhle 3.2. C'ocl coefficients - IO-
Page 103 and 104:
3.5. CONCLUSIONIn this approach a h
Page 105 and 106:
to the corrective control of voltag
Page 107 and 108:
) OLTC tap position (discrete varia
Page 109 and 110:
4.5. SIMULATION RESULTSThe proposed
Page 111 and 112:
The continuous AVR operating values
Page 113 and 114:
Table 4.5. Optimal control variable
Page 115 and 116:
As discussed earlier, if the index
Page 117 and 118:
CHAPTER 5SWARM INTELLIGENCE BASED O
Page 119 and 120:
5.2. APPROACH TO QUANTIFY THE BENEF
Page 121 and 122:
5.2.2. Line loss redoction indexAno
Page 123 and 124:
53.1. Objective FunctionThc propose
Page 125 and 126:
various capacities. The voltage pro
Page 127 and 128:
~ .-0.2072 0.7309 26.910.2039 0.719
Page 129 and 130:
ITable 5.8. Solution of improvement
Page 131 and 132:
capacitive operation and minimum po
Page 133 and 134:
~xtensively used in present DVR con
Page 135 and 136:
6.3.2.1. DVR Power FlowThe power fl
Page 137 and 138:
(X'+Y:l0' =[i.. 1; ! ., I;]"IV,,COS
Page 139 and 140:
Subject toL,oad voltage advance ang
Page 141 and 142:
DVR is 99.906 KW for a balanced sag
Page 143 and 144:
GenerationsFig. 6.3.
Page 145 and 146:
CHAPTER 7CONCLUSIONThe thesis has i
Page 147 and 148:
(vi)(vii)obtained using PSO based m
Page 149:
APPENDICESAPPENDIX - ADATA FOR IEEE
Page 153 and 154:
Table A.3. Generator cost and Emins
Page 155 and 156:
APPENDIX - BDATA FOR 6 UNIT TEST SY
Page 157 and 158:
APPENDIX - CDATA FOR lIUNlT TEST SY
Page 159:
APPENDIX - DDATA FOR TAIPOWER 40-UN
Page 162 and 163:
A transmission llne on a per phase
Page 164 and 165:
y IS propagation constant and 1 is
Page 166 and 167:
APPENDIX - FSTANDARD - 5 BUS SYSTEM
Page 168 and 169:
APPENDM - GDATA FOR IEEE-14 BUS TES
Page 170 and 171:
Tabk G.2. Bus data and load flow re
Page 172:
APPENDIX - HDATA FOR AN IEEE-57 BUS
Page 178 and 179:
Table H3. Transformer tap setting d
Page 180 and 181:
Tabk 1.1. Sitar and location of dif
Page 182 and 183:
Tabk 1.3. Bus data
Page 184:
Table J.2. Bus data- - ---- -- -L.o
Page 187 and 188:
L. S. Vargas. V. H. Quintana, and V
Page 189 and 190:
J. F. Dapao and H. M. Merill. "Opti
Page 191 and 192:
P. H. Chen and H. C. Chang, "Large
Page 193 and 194:
M. Tripathy and S. Mishra, "Bacteri
Page 195 and 196:
[87] D. B. Fogel. "Evolut~onary Com
Page 197 and 198:
[I071 Costas Voumas and M~chael Kar
Page 199 and 200:
[I251 W. G. Scott, "M~cm-turb~ne ge
Page 201 and 202:
[I431 F. Jurado and Perez Hidalgo.
Page 203 and 204:
LIST OF PUBLICATIONSlnternational J
Page 205:
VITAEP.Ajay-D-Vimal Raj born in Kar
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