一种基于改进PSO算法的配电网无功优化研究  

Research on Reactive Power Optimization of Distribution Network Based on Improved PSO Algorithm

作　　者：王琳语 WANG Linyu(College of Electrical and Control Engineering,Liaoning Technical University,Huludao,Liaoning 125000,China)

机构地区：[1]辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛125000

出　　处：《东北电力技术》2025年第4期44-47,共4页Northeast Electric Power Technology

摘　　要：随着新能源的大规模接入,配电网络变得更加复杂多变。为应对高比例可再生能源接入配电网所引发的无功问题,提出了一种基于改进粒子群优化(particle swarm optimization, PSO)算法的无功优化调度模型。该模型旨在优化多能系统的运行成本,减少网络损耗,并通过无功补偿、储能调节和能量转换等手段,确保电网的经济性和运行安全。首先,对传统PSO算法进行改进,应用混沌初始化并结合反向学习策略生成PSO算法的初始种群来优化初始种群,让算法加速收敛;其次,应用非线性策略和黄金正弦算法搜索策略,对惯性权重公式、学习因子和位置更新公式进行改良,提升了算法的搜索效率;最后,将传统PSO算法和改进PSO算法应用于IEEE 33节点进行仿真对比,仿真结果验证了策略的有效性。With the large-scale integration of new energy sources,the distribution network becomes increasingly complex and variable.In order to deal with the reactive power problem caused by the high proportion of renewable energy connected to the distribution network,it proposes a reactive power optimization scheduling model based on the improved particle swarm optimization(PSO)algorithm.This model aims to optimize the operating cost of multi-energy systems,reduce network losses.Through reactive power compensation,energy storage regulation and energy conversion,it ensures the economy and operation safety of the power grid.Firstly,it improves the traditional PSO algorithm,applies chaotic initialization in conjunction with a reverse learning strategy to generate the initial population,optimizing it to accelerate convergence.Secondly,it uses nonlinear strategies and a golden sine algorithm search strategy to enhance the inertia weight formula,learning factors,and position update formulas,thereby improving the search efficiency of the algorithm.Finally,the traditional PSO algorithm and the improved PSO algorithm are applied to simulation comparisons on the IEEE 33-node system.The simulation result verifies the effectiveness of the improvements.

关 键 词：配电网 无功优化 改进粒子群算法 

分 类 号：TM73[电气工程—电力系统及自动化]