DocumentCode :
3012973
Title :
Multi-objective Optimal Design for Hybrid Active Power Filter Based on Composite Method of Genetic Algorithm and Particle Swarm Optimization
Author :
Jiang You-hua ; Liao Dai-fa
Author_Institution :
Sch. of Comput. & Inf. Technol., Shanghai Univ. of Electr. Power, Shanghai, China
Volume :
2
fYear :
2009
fDate :
7-8 Nov. 2009
Firstpage :
549
Lastpage :
553
Abstract :
A new mixed algorithm of genetic theory and particle swarm optimization (GA-PSO) have been proposed in this paper to tackle the optimal design problem of hybrid active power filter (HAPF) in its parameter design and investment optimization, considering the better convergence of genetic theory and fast convergence of particle swarm optimization. It takes the original investment, the capacity of reactive power compensation and harmonic distortion as three objectives, and penalty function theory have been used to convert multi-objective design problems into single-objective design problems. Finally a HAPF simulation under the background of PSCAD/EMTDC has been analyzed, the results show that the proposed optimal design method of HAPF can save cost, enhance performance-price ratio and filtering performance.
Keywords :
active filters; genetic algorithms; harmonic distortion; hybrid power systems; particle swarm optimisation; power harmonic filters; power system harmonics; reactive power; HAPF simulation; composite method; genetic algorithm; genetic theory; harmonic distortion; hybrid active power filter; investment optimization; multiobjective optimal design; parameter design; particle swarm optimization; penalty function; performance-price ratio; reactive power compensation; Active filters; Algorithm design and analysis; Analytical models; Convergence; Design optimization; Genetic algorithms; Harmonic distortion; Investments; Particle swarm optimization; Reactive power; hybrid active power filters; multi-objective; particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
Type :
conf
DOI :
10.1109/AICI.2009.295
Filename :
5375919
Link To Document :
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