基于多目标飞蛾算法的电力系统无功优化研究  

Multi-objective Moth-flame Optimization Algorithm Based Optimal Reactive Power Dispatch for Power System

作　　者：李伟琨[1] 阙波[2] 王万良[1] 倪立洲 

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023 [2]国网浙江省电力公司电力控制研究所,杭州310000 [3]杭州天丽科技有限公司自动化控制研究所,杭州310000

出　　处：《计算机科学》2017年第B11期503-509,共7页Computer Science

基　　金：国家电网公司科技项目(SGZJ0000BGJS1500460)资助

摘　　要：鉴于电力需求的日益增长与传统无功优化方法的桎梏,如何更加合理有效地解决电力系统的无功优化问题逐渐成为了研究的热点。提出一种多目标飞蛾扑火算法来解决电力系统多目标无功优化的问题,算法引入固定大小的外部储存机制、自适应的网格和筛选机制来有效存储和提升无功优化问题的帕累托最优解集,算法采用CEC2009标准多目标测试函数来进行仿真实验,并与两种经典算法进行性能的对比分析。此外,在电力系统IEEE 30节点上将该算法与MOPSO,NGSGA-Ⅱ算法的求解结果进行比较分析的结果表明,多目标飞蛾算法具有良好的性能,并在解决电力系统多目标无功优化问题上具有良好的潜力。In view of the increasing power energy demand and the drawback of conventional reactive power optimization methods,how to effectively solve the reactive power optimization has become a hot spot in power research.This paper proposed a multi-objective model of reactive power optimization problems in power system and a multi-objective mothflame optimization algorithm (MOMFA) to optimize problems with multiple objectives for the first time.A fixed-sized external archive,grid and select mechanism are integrated to the MOMFA for maintaining and improving the pareto optimal solutions.The proposed algorithm is compared with two well-known algorithms on CEC multi-objective optimization test problems.Moreover,the proposed algorithm was simulated in real power system data and compared with two well-known algorithms:multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm version 2 (NSGA-Ⅱ).The results demonstrate that the proposed algorithm is outperforms other algorithms in reactive power optimization.

关 键 词：多目标优化 进化算法 无功优化 飞蛾扑火 电力系统 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]