混合粒子群算法用于配电网优化的研究  

Research on Hybrid Particle Swarm Optimization for Distribution Network Optimization

作　　者：陆鑫昊 宋威[1,3] LU Xin-hao;SONG Wei(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China;State Grid Jiangsu Electric Power Maintenance Branch Company,Nanjing 211102,China;Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122 [2]江苏省电力有限公司检修分公司,南京211102 [3]江南大学江苏省模式识别与计算智能工程实验室,江苏无锡214122

出　　处：《小型微型计算机系统》2020年第8期1788-1792,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61673193)资助;中国博士后科学基金项目(2017M621625)资助;江苏省自然科学基金项目(BK20181341)资助。

摘　　要：配电网拓扑结构的优化是配电网运行中的一项重要任务,在正常工况或异常工况下通过改变开关状态位置来实现.本文以网络的有功损失最小为目标函数,并将节点电压、线路容量、径向结构作为约束条件,提出了一种混合粒子群优化算法,有效地确定了配电网的最优配置.混合粒子群优化算法并不需要通过原粒子群算法公式来更新粒子位置,而是引入了遗传算法的交叉与变异操作,通过粒子自身个体极值与群体极值的交叉以及粒子自身的变异来更新粒子位置,从而加快了算法的搜索速度.将该方法应用于IEEE33配电网的优化计算,结果表明,本文的方法每次都能找到全局最优值.The optimization of distribution network topology is an important task in the operation of distribution network.Taking the minimum active power loss of the network as the objective function,and taking node voltage,line capacity and radial structure as the constraint conditions,this paper proposes a hybrid particle swarm optimization algorithm,which effectively determines the optimal configuration of the distribution network.Hybrid particle swarm optimization algorithm does not need to update the particle position through the original particle swarm algorithm formula,but introduces the crossover and mutation operation of genetic algorithm,which updates the particle position through the crossover of the individual extreme value of the particle and the group extreme value of the particle and the mutation of the particle itself,thus speeding up the search speed of the algorithm.This method is applied to the optimization calculation of IEEE33 distribution network,and the results show that the global optimal value can be found every time.

关 键 词：粒子群算法 遗传算法 优化 配电网 有功损耗 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]