performance evaluation of swarm intelligence based power system ...

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Venkatesh et al. [46] have built an EP algorithm to solve the CEED pmblemwith line flow constraints. The line flows in MVA have been computed directly fromthe Newto=Raphson method. A novel modified price penalty factor has beenintroduced to find the exact economic emission fuel cost with respect to the loaddemand. The test results of IEEE-14, -30 and -1 18 bus systems have been comparedwith that of other evolutionary computing techniques.Abido [47] has derived a Pareto-based multiobjective evolutionary algorithm(MOEA) for solving an environmental/economic electric power dispatch pmblem.This fuzzy-based hierarchical clustering technique has been implemented in order toobtain the best solution. The test results of an IEEE-30 bus system have beencompared with that of other traditional multiobjective optimization techniques.1.3.1.3. Economic Load Dispatch with Prohibited Operating ZonesWalters et al. 1481 have developed a genetic algorithm to solve the economicdispatch problem with valve-point effects. This algorithm has utilized payoffinformation of the candidate solutions to evaluate their optimality. The test results ofthree units system have been compared with that of dynamic programming method.Wong et al. [49] have built an incremental genetic algorithm based approachfor the determination of global or near-global optimum solution. Another techniquethat incorporates both incremental genetic theory and simulated annealing has servedto determine the economic loadings of 13 generators in a practical power system withthe effects of valve-point loading and ramping characteristics. The test results havebeen found to yield better results when compared with that of simulated annealingbased method.Chen et al. [50] have presented a GA-based method that uses the incrementalcost of encoded parameter of the system for solving the ED problem taking intoaccount the network losses, ramp rate limits, valve-point zone and prohibitedoperating zone. The numerical results of the method for a large scale 40-unit systemhave been compared with that of lambda-iteration method.
Fung et al. [51] have formulated an integrated parallel genetic algorithmincorporating tabu search (TS) and simulated annealing for solving the ED problem.The parallel computing platform has been based on a network of interconnectedpersonal computers (PCs) using TCPAP socket communication facilities. The testresults of a practical power system have been obtained to compute the optimal loadingof 13 generators.El-Gallad et al. [52] have adapted a PSO technique to solve the traditionaleconomic dispatch problem. The objective function has been formulated as acombination of piecewise quadratic cost functions with nondifferential regions,instead of adopting a single convex function for each generating unit. This innovationhas served to incorporate practical operating conditions, such as valve-point effectsand fuel types. The effectiveness of the algorithm has been tested on a three unitsystem and the results have been compared with that of a numerical method.El-Gallad et al. [53] have added new constraints to the problem by introducingsystem spinning reserve and generator prohibited operating zones. In this formulation,they have included the same constraints but considered a single convex cost function1521. The test results of a 15-unit system in which four units with prohibited operatingzones have been compared with for both conventional method and the Hopfield neuralnetwork.Lai et al. 1541 have applied PSO to solve economic dispatch (ED) of units withnonsmooth input-output characteristic functions. The test results of an IEEE-30 bussystem with six generating units have been compared with that of evolutionaryprogramming (EP).Victoire et al. have extended Gaing's research by forming a hybrid optimizerto tackle the same problem [55]. They have used sequential quadratic programming tofine-tune the PSO search in finding the optimal solution. The feasibility has beenillustrated by conducting case studies on a 10-unit system with valve-point effects forthree different load-demand patterns and the results have been compared with thatobtained using the EP-SQP method.
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: 1.3.1. ECONOMIC LOAD DISPATCH PROBL
Page 35 and 36: een handled by BPSO, whereas the ec
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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