基于改进非线性惯性权重的粒子群优化算法的智能配电网故障定位方法研究  

Research on Smart Distribution Network Fault Location Method Based on Particle Swarm Optimization Algorithm with Improved Nonlinear Inertia Weights

作　　者：戚乐乐 王玉林 宋冰 Qi Lele;Wang Yulin;Song Bing(School of Electrical and Control Engineering,Liaoning University of Engineering and Technology,Huludao Liaoning 125105,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125105

出　　处：《现代工业经济和信息化》2024年第11期126-127,130,共3页Modern Industrial Economy and Informationization

摘　　要：在配电网运行与维护中,故障定位问题一直是关注焦点。然而,传统的故障定位方法在面对分布式电源等新挑战时存在局限性。为克服这些问题,针对粒子群算法(PSO)进行改进,提出了一种改进策略。该策略利用非线性惯性权重函数,实现了对算法搜索过程的优化,从而提高了全局搜索能力和收敛速度。结合配电网故障定位流程,提出了基于改进粒子群算法的故障定位模型,并通过MATLAB仿真实验建立含有分布式电源的IEEE33节点配电网模型,验证了方法的有效性。实验结果表明,所提方法在故障定位准确性和收敛速度方面均取得了显著的优化效果,为提高配电网故障定位的效率和准确性提供了一种新思路和解决方案。Fault localization has always been the focus of attention in distribution network operation and maintenance.However,traditional fault localization methods have limitations when facing new challenges such as distributed power supply.To overcome these problems,an improved strategy is proposed for the PSO.The strategy utilizes the nonlinear inertia weight function to optimize the search process of the algorithm,thus improving the global search capability and convergence speed.Combined with the fault localization process of distribution network,the fault localization model based on the improved particle swarm algorithm is proposed,and the IEEE33 node distribution network model containing distributed power supply is established through MATLAB simulation experiments to verify the effectiveness of the method.The experimental results show that the proposed method has achieved significant optimization effects in terms of fault location accuracy and convergence speed,which provides a new idea and solution for improving the efficiency and accuracy of fault location in distribution networks.

关 键 词：改进非线性惯性权重 二进制粒子群优化算法 配电网 故障定位 分布式电源 

分 类 号：TM76[电气工程—电力系统及自动化]