基于群搜索算法的电力系统无功优化  

Reactive power optimization based on group search optimizer

作　　者：刘华臣[1] 王锡淮[1] 肖健梅[1] 王海锋[1] 

机构地区：[1]上海海事大学电气自动化系,上海201306

出　　处：《电力系统保护与控制》2014年第14期93-99,共7页Power System Protection and Control

基　　金：国家自然科学基金项目(61175044);上海市教委科研创新重点项目(12ZZ158);上海海事大学校基金项目(20130054)~~

摘　　要：为了提高电力系统的运行效率和经济性能,用群搜索优化算法(Group Search Optimizer)对电力系统各控制变量进行合理配置,以此减少电力系统无功损耗。群搜索优化算法是一种新兴的群智能优化算法,该算法把群成员分为发现者、追随者和游荡者三种,其中游荡者的位置随机选定,这有效地避免了其他算法容易陷入局部最小值问题。选定电力系统中无功投入量、电压变比、发电机端电压等作为控制变量,通过群搜索优化算法对控制变量进行迭代计算和潮流计算,最终计算出最小的网络损耗及其对应的控制变量值。最后用Matlab7.6对IEEE-14、30节点系统进行仿真,并与其他群智能优化算法进行对比,结果显示,群搜索算法的收敛较快且稳定,最终证明了群搜索算法对无功优化的优越性。In order to increase the operating efficiency and economic performance, this paper uses the group search optimizer to optimize the control variable of power systems so as to decrease the reactive loss. Group search optimizer is a new kind of swarm intelligence optimization algorithm, which divides group members into three types： producer, scrounger and ranger. The location of rangers is random, which can avoid entrapment in local minima effectively. The input of reactive power and the voltage transformer ratios, and the terminal voltage of generator are designated as control variables, after the iterative computations and power flow calculation by using the swarm search optimization algorithm, the minimum of the reactive loss and the corresponding control variable can be figured out. At last, the IEEE14-bus system and IEEE30-bus system are simulated with Matlab7.6 and contrasted with the others optimizers, the results show that the convergence rate of the group search optimizer is fast and stable, and superior in reactive power optimization. This work is supported by National Natural Science Foundation of China （No. 61175044）.

关 键 词：群搜索优化算法 电力系统无功优化 潮流计算 IEEE节点系统 

分 类 号：TM714.3[电气工程—电力系统及自动化]