改进型粒子群算法的电力系统无功优化研究  

Research on Reactive Power Optimization of Power System Based on Improved Particle Swarm Optimization

作　　者：李昂[1] 董潇阳 纪瑾 李兴 LI Ang;DONG Xiaoyang;JI Jin;LI Xing(College of Electrical Engineering,Shaanxi University of Technology,Hanzhong 723000,China)

机构地区：[1]陕西理工大学电气工程学院,陕西汉中723000

出　　处：《电工技术》2022年第3期11-13,17,共4页Electric Engineering

基　　金：陕西省教育厅专项科研计划项目(编号15JK1125)。

摘　　要：针对粒子群算法(PSO)应用电力系统无功优化时本身特性易导致计算结果不精确的问题,首先将系统有功网损最小值作为目标函数模型;其次调整该算法的惯性权重(ω)和学习因子(c);再引入免疫算法,结合两种算法的优势进行融合,发现该方法可在一定程度弥补粒子群算法原本的缺陷,在全局范围内搜索确定最优解;最后对改进算法应用MATLAB进行检验,结果验证了理论分析的正确性。研究结果表明该方法可有效降低系统网损,提高电能质量。When the particle swarm optimization(PSO)is applied to power system reactive power optimization,its own characteristics will lead to inaccurate calculation results.To solve this problem,firstly,the minimum value of the system′s active power loss is set as the objective function model.Secondly,the inertia weight(ω)and learning factor(c)of the algorithm are adjusted.Then the immune algorithm is introduced to fuse the advantages of the two algorithms.It is found that this method can make up for the original defects of particle swarm optimization algorithm to a certain extent,and search and determine the optimal solution in the global range.Finally,the improved algorithm is tested by MATLAB,and the results verify the correctness of the theoretical analysis.The results show that this method can effectively reduce the system network loss and improve the power quality.

关 键 词：无功优化 粒子群算法 免疫算法 惯性权重 学习因子 

分 类 号：TM71[电气工程—电力系统及自动化]