[1]李圣清,李永安,罗晓东,等.基于菌群-粒子群算法的混合有源滤波器中无源滤波器多目标优化设计[J].控制与信息技术(原大功率变流技术),2011,(04):13-16.[doi:10.13889/j.issn.2095-3631.2011.04.001]
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基于菌群-粒子群算法的混合有源滤波器中无源滤波器多目标优化设计()
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《控制与信息技术》(原《大功率变流技术》)[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2011年04期
页码:
13-16
栏目:
“电能质量及其控制”专刊
出版日期:
2011-08-05

文章信息/Info

Title:
Multi-objective Optimal Design for Passive Part of Hybrid Active Power Filter Based on Bacterial Foraging and Particle Swarm Optimization
文章编号:
1671-8410(2011)04-0013-04
作者:
李圣清李永安罗晓东曾黎琳何政平
湖南工业大学
关键词:
优化设计无源滤波器菌群- 粒子群优化算法
Keywords:
Optimal design passive power filter BFO-PSO algorithm
分类号:
TN713
DOI:
10.13889/j.issn.2095-3631.2011.04.001
摘要:
采用粒子群优化算法(particle swarm optimization,PSO)和菌群优化算法(bacterial foraging optimization, BFO)相结合的、带有随机惯性因子和异步时变学习因子的改进型BFO-PSO 优化算法,解决混合型有源滤波器中无源滤波器参数优化设计问题。通过将滤波器的无功补偿容量、补偿后滤波效果及初期投资成本作为优化目标,采用重要目标加动态常数制约法给出综合适应度函数,进而求解多目标优化问题。仿真验证了理论分析和设计的正确性,相关设计方法也可为其他类型的无源滤波器的优化设计提供借鉴。
Abstract:
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.

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备注/Memo

备注/Memo:
收稿日期:2011-05 -28
作者简介:李圣清(1 9 6 1 - ),男,博士,教授,硕士生导师,主要从事电力系统谐波的分析与治理、电能质量调节与控制的研究工作。
基金项目:国家自然科学基金资助项目(5 1 0 7 7 0 4 6 );湖南省自然科学基金项目(0 9 J J 6 0 7 0 );湖南省教育厅重点科研项目(0 9 A 0 2 2 );湖南工业大学研究生创新基金项目(C X 1 1 1 3 )
更新日期/Last Update: 2017-09-26