Multi-objective Optimal Design for Passive Part of Hybrid Active Power Filter Based on Bacterial Foraging and Particle Swarm Optimization

With combination of particle swarm optimization algorithm and bacterial foraging optimization, an improved BFO-PSO optimized algorithm based on the random inertia factor and asynchronous time-dependent learning factor is used to solve optimal design,s problems of passive filter parameters for hybrid...

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Bibliographic Details
Main Authors: 李圣清, 李永安, 罗晓东, 曾黎琳, 何政平
Format: Article
Language:zho
Published: Editorial Office of Control and Information Technology 2011-01-01
Series:Kongzhi Yu Xinxi Jishu
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Online Access:http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2095-3631.2011.04.001
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Summary:With combination of particle swarm optimization algorithm and bacterial foraging optimization, an improved BFO-PSO optimized algorithm based on the random inertia factor and asynchronous time-dependent learning factor is used to solve optimal design,s problems of passive filter parameters for hybrid active filter. It takes the capacity of reactive power compensation, the harmonic effect after compensation and the original investment cost as three objectives, and restricts the important goal and dynamic constants as a method to achieve comprehensive fitness function, and then solves the multi-objective optimization problem. Simulation result verifies the correctness of the mentioned theory and design. Such design method can be used as a reference for design optimization of other type passive power filters.
ISSN:2096-5427