基于自学习粒子群优化算法的单相动态电压恢复器补偿策略  

Compensation Strategy of Single Phase Dynamic Voltage Restorer Based on Self-learning Particle Swarm Optimization

作　　者：韩文花[1] 沈晓晖[1] 徐俊[1] 陈旭[1] 贺帅鹏[1] 

机构地区：[1]上海电力学院自动化工程学院,上海200090

出　　处：《广东电力》2013年第8期5-11,共7页Guangdong Electric Power

基　　金：国家自然科学基金资助项目(51107080);上海市级大学生创新活动计划项目(2012-10256-047)

摘　　要：针对电压凹陷发生概率较高、危害大,严重影响电能质量的问题,通过理论分析和仿真实验,研究单相动态电压恢复器(dunamic voltage restorer,DVR)的补偿策略来改善电压凹陷,其内容主要包括对单相DVR已有的传统补偿策略进行理论分析与比较,并在此基础上提出一种基于自学习粒子群优化算法(self-learning particle swarm optimization,SLPSO)的优化补偿策略,通过仿真实验验证了该策略的正确性和可行性。Aiming at problems of high probability of occurrence and big harm of voltage sag which may seriously impact power quality, this paper studies compensation strategies of single phase dynamic voltage restorer by theoretical analysis and sim- ulation experiment to improve voltage sag. It proceeds theoretical analysis and comparison for existing traditional compensa- tion strategies of single phase dynamic voltage restorer and introduces one new optimizing compensation strategy based on self-learning particle swarm optimization. By simulation experiment, it verifies validity and feasibility of this strategy.

关 键 词：动态电压恢复器 电压凹陷 补偿策略 自学习粒子群优化算法 

分 类 号：TM76[电气工程—电力系统及自动化]