Implementation of GCPSO for Multi-objective VAr Planning with SVC and Its Comparison with GA and PSO

Author

Farsangi, Malihe M. ; Nezamabadi-Pour, Hossein ; Lee, Kwang Y.

Author_Institution

Shahid Bahonar Univ. of Kerman, Kerman

fYear

2007

fDate

5-8 Nov. 2007

Firstpage

1

Lastpage

6

Abstract

In this paper, Guaranteed Convergence Particle Swarm Optimization (GCPSO) Algorithm is used for VAr planning with the Static Var Compensators (SVC) in a large-scale power system. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem is solved by the fuzzy GCPSO and the results are compared with those obtained by the Particle Swarm Optimization (PSO) and Genetic Algorithm

Keywords

fuzzy set theory; genetic algorithms; particle swarm optimisation; power system planning; power system stability; static VAr compensators; PSO; SVC; fuzzy performance indices; genetic algorithm; guaranteed convergence particle swarm optimization; large- scale power system; multiobjective optimization problem; multiobjective var planning; particle swarm optimization; static var compensators; voltage stability; Convergence; Cost function; Genetic algorithms; Optimization methods; Particle swarm optimization; Power system planning; Power system stability; Reactive power; Static VAr compensators; Voltage; SVC; fuzzy performance indices; genetic algorithm; guaranteed convergence particle swarm optimization; multiobjective optimization; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. International Conference on

Conference_Location

Toki Messe, Niigata

Print_ISBN

978-986-01-2607-5

Electronic_ISBN

978-986-01-2607-5

Type

conf

DOI

10.1109/ISAP.2007.4441632

Filename

4441632