一种求解最优潮流的改进灰狼优化算法  

An improved grey wolf optimization algorithm for solving optimal power flow

作　　者：王恒 杨婷 WANG Heng;YANG Ting(School of Information Engineering,Tongren Polytechnic College,Tongren 554300,Guizhou,China)

机构地区：[1]铜仁职业技术学院信息工程学院,贵州铜仁554300

出　　处：《智能计算机与应用》2024年第3期46-53,共8页Intelligent Computer and Applications

基　　金：铜仁市科学技术局基础科学研究项目(铜市科研(2022)72号)。

摘　　要：最优潮流是电力系统最关键的问题之一,本文采用一种求解最优潮流的改进灰狼优化算法(LMGWO)求解最优潮流(OPF)问题,该算法引入算术优化算法(Arithmetic Optimization Algorithm,AOA)中的乘除算子,利用带透镜成像的反向学习策略增强最优个体的多样性,提高算法跳出局部最优的能力。通过与几种常用的算法进行对比实验表明:本文提出的LWG-WO算法是有竞争力的,总体上优于对比算法;LMGWO算法在最小化燃料成本、有功输电损耗和改善电压偏差方面更有效地找到了最优潮流(OPF)问题的最优解。Optimal power flow is one of the most critical problems in power system.In this paper,an improved Grey Wolf Optimization Algorithm(LMGWO)is used to solve the optimal power flow(OPF)problem.In this algorithm,multiplication and division operators in the Arithmetic Optimization Algorithm(AOA)are introduced.The reverse learning strategy with lens imaging is used to enhance the diversity of optimal individuals and improve the ability of the algorithm to jump out of the local optimal.Through comparative experimental analysis of several commonly used algorithms,the proposed LWGWO algorithm is competitive and generally superior to recent algorithms.The experimental results show that LMGWO algorithm can find the optimal solution of OPF problem more effectively in terms of minimizing fuel cost,active power transmission loss and improving voltage deviation.

关 键 词：灰狼优化算法 最优潮流 算术优化算法 燃料成本 有功输电损耗 

分 类 号：TE341[石油与天然气工程—油气田开发工程]