DocumentCode :
3164693
Title :
Adaptive Particle Swarm Optimization Algorithm for Power System Reactive Power Optimization
Author :
Li, Dan ; Gao, Liqun ; Lu, Shun ; Ma, Jia ; Li, Yang
Author_Institution :
Northeastern Univ., Shenyang
fYear :
2007
fDate :
9-13 July 2007
Firstpage :
4733
Lastpage :
4737
Abstract :
Aiming at the precocious convergence problem of particle swarm optimization algorithm, adaptive particle swarm optimization(APSO) algorithm was presented. In this algorithm, the notion of species was introduced into population diversity measure. The species technique is based on the concept of dividing the population into several species according to their similarity. The inertia weight was nonlinearly adjusted by using population diversity information at each iteration step. Velocity mutation operator and position crossover operator were both introduced and the global performance was clearly improved. The algorithm had been applied to reactive power optimization. The simulation results of the standard IEEE-30-bus power system had indicated that APSO was able to undertake global search with a fast convergence rate and a feature of robust computation. It was proved to be validity, fast convergence and computation efficiency during the reactive power optimization.
Keywords :
IEEE standards; particle swarm optimisation; power systems; reactive power; IEEE-30-bus power system; adaptive particle swarm optimization; population diversity information; population diversity measure; position crossover operator; power system reactive power optimization; velocity mutation operator; Control systems; Convergence; Fuzzy systems; Genetic mutations; Mathematical model; Particle swarm optimization; Power system modeling; Power systems; Reactive power; Reactive power control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2007. ACC '07
Conference_Location :
New York, NY
ISSN :
0743-1619
Print_ISBN :
1-4244-0988-8
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2007.4282511
Filename :
4282511
Link To Document :
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