基于改进果蝇优化算法的无功优化  

Reactive power optimization based on improved fruit fly optimization algorithm

作　　者：冯云斌 崔雪[1] 王恒[1] 彭政[1] 周斌[1] FENG Yunbin CUI Xue WANG Heng PENG Zheng ZHOU Bin(School of Electrical Engineering, Wuhan University,Wuhan 4a0072, Chin)

机构地区：[1]武汉大学电气工程学院,湖北武汉430072

出　　处：《武汉大学学报（工学版）》2017年第4期560-565,共6页Engineering Journal of Wuhan University

基　　金：国家自然科学基金项目(编号:51177112)

摘　　要：将改进果蝇优化算法运用于无功优化领域,为电力系统的无功优化计算提供了一种新的算法.通过对迭代步长进行自适应调整可以有效避免果蝇算法可能陷入局部最优的问题,同时还能提高收敛精度.在无功优化模型中,对控制变量进行归一化处理,使得量纲一致;将约束条件以罚函数的形式并入目标函数中,实现对状态变量的限制.以IEEE30节点系统和IEEE57节点系统为例,分别基于果蝇优化算法(FOA)、改进果蝇优化算法(IFOA)和遗传算法(GA)进行了无功优化计算,结果表明改进果蝇优化算法(IFOA)具有更好的优化效果和计算速度,更加接近全局最优值,采用该算法解决无功优化问题效果很好.Improved fruit fly optimization algorithm is applied to power system reactive power optimization field,which is a new solution to solve the problem of reactive power optimization. Adopting self-adapting adjustment of the iteration step can effectively avoid the problem that fruit fly optimization algorithm may fall into local optimization; at the same time, it can improve the calculation precision. In the reactive power optimization model, in order to keep the same dimension, control variable is normalized. The constraint condition is added into the objective function in the form of a penalty function, which can limit control vari- able. The paper takes IEEE30-bus system and IEEE57-bus system as examples, conducts reactive power op- timization calculation,which is based on fruit fly optimization algorithm（FOA）, improved fruit fly optimi- zation algorithm（IFOA） and genetic algorithm（GA）. The results show that the improved fruit fly optimiza- tion algorithm has better optimization effect and calculation speed; what is more,it is closer to the global optimal value. Thus,improved fruit fly optimization algorithm can be used to solve the reactive power optimization problem.

关 键 词：电力系统 无功优化 果蝇优化算法 迭代步长自适应调整 

分 类 号：TM714.3[电气工程—电力系统及自动化]