DocumentCode :
1947358
Title :
A Messy Genetic Algorithm Based Optimization Scheme for SVC Placement of Power Systems under Critical Operation Contingence
Author :
Huang, J.S. ; Negnevitsky, Michael
Author_Institution :
Sch. of Comput. & Math, Univ. of Western Sydney, Sydney, NSW
Volume :
1
fYear :
2008
fDate :
12-14 Dec. 2008
Firstpage :
467
Lastpage :
472
Abstract :
In the paper the authors present a messy genetic-algorithm-based optimization scheme for voltage stability enhancement of power systems under critical operation conditions. The placement of SVCs in a power system has been posed as a multi-objective optimization in terms of maximum worst-case reactive margin, highest load voltages at the critical operating points, minimum real power losses and lowest device costs. During the genetic algorithm search for the optimal solution, the most critical disturbance scenario is estimated with the configuration of the original power system and each candidate SVC placement. By using this estimation, the SVC placement can be greatly simplified. With a fuzzy performance index, the multi-objective optimization can be further transformed into a constrained problem with a single non-differentiable objective function containing both continuous and discrete variables.
Keywords :
fuzzy set theory; genetic algorithms; power system stability; static VAr compensators; fuzzy performance index; messy genetic algorithm based optimization scheme; multiobjective optimization; power losses; power system stability; static VAR compensators; voltage stability enhancement; Cost function; Genetic algorithms; Power system planning; Power system security; Power system simulation; Power system stability; Power systems; Reactive power; Static VAr compensators; Voltage; messy genetic algorithm; multi-objective optimization; non-linear programming; voltage stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
Type :
conf
DOI :
10.1109/CSSE.2008.1148
Filename :
4721788
Link To Document :
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