基于模糊聚类和学习自动机的多目标无功优化  

Multi-Objective Reactive Power Optimization by Fuzzy Cluster and Learning Automata

作　　者：王玉荣[1] 万秋兰[1] 陈昊[1] 

机构地区：[1]东南大学电气工程学院,江苏省南京市210096

出　　处：《电网技术》2012年第7期224-230,共7页Power System Technology

摘　　要：电力系统无功补偿需确定无功补偿的选点及具体的补偿容量。基于模糊聚类的方法寻找系统薄弱节点,得到候选节点信息,动态聚模糊类过程中采用了U/U0指标、指标和电压偏移指标。综合考虑发电成本和无功投入成本最小、电压偏移最小和有功网损最小化,建立了候选无功补偿节点的多目标优化模型,并采用学习自动机法获得优化问题的最优权衡解。采用模糊聚类法和学习自动机法对IEEE 57节点测试系统进行算例分析,分析结果表明了所提方法的有效性。To perform reactive power compensation,it is necessary to select the site where the compensator is configured and the needed compensation capacity.The weak nodes in power system are searched by fuzzy cluster and the information of candidate nodes is obtained;during the dynamic fuzzy cluster,the indices of U/U0,？ and voltage deviation are applied.Synthetically considering the minimum generation cost and reactive compensation cost,the minimum voltage deviation and minimum active network loss,a multi-objective optimization model for candidate nodes,where reactive compensator may be configured,is built and the optimal trade-off solution of the optimization problem is obtained by learning automata.Applying fuzzy cluster and learning automata to IEEE 57-bus system,calculation results show that the proposed method is effective.

关 键 词：模糊聚类 多目标优化 学习自动机 无功优化 

分 类 号：TM72[电气工程—电力系统及自动化]