基于量子粒子群优化算法HAPF参数的多目标优化  

Multi-object optimization of hybrid active power filter based on quantum-behaved particle swarm optimization

作　　者：张国荣[1] 李丹[1] 

机构地区：[1]合肥工业大学教育部光伏系统工程研究中心,安徽合肥230009

出　　处：《合肥工业大学学报（自然科学版）》2010年第8期1152-1156,共5页Journal of Hefei University of Technology：Natural Science

基　　金：国家自然科学基金资助项目(50777015;50837001);教育部科学技术研究重大资助项目(306004)

摘　　要：文章用一种量子粒子群优化算法对混合型有源电力滤波器(HAPF)参数进行多目标优化设计,优化目标包括系统的投资成本、无功补偿和滤波效果等,该算法解决了HAPF系统的参数匹配以及无源、有源容量的分配问题。实验中该算法寻优速度较快,THDU、THDI分别降低到1.9%和2.0%,APF容量仅占混合滤波器容量的8.1%,使HAPF在电能质量综合治理中实现了既有效又经济的目的。The quantum-behaved particle swarm optimization algorithm is applied in multi-object optimization design for the parameters of the hybrid active power filter（HAPF）.The optimization objectives include investment cost of the system,reactive power compensation and filtering effects,etc.This algorithm solves the match of the HAPF systematical parameters as well as passive and active capacity allocation problems.In the experiment,the speed of seeking for the optimized result by this algorithm is faster.The total voltage and current harmonic distortion rates reduced to 1.9% and 2.0% respectively.APF capacity accounts for only 8.1% of the capacity of hybrid filters.The optimum design result makes HAPF achieve both efficiency and economy purposes in the integrated management of power quality.

关 键 词：混合型有源电力滤波器 量子粒子群优化算法 拓扑结构 

分 类 号：TM761.12[电气工程—电力系统及自动化]