基于改进麻雀算法的配电网故障定位  

Distribution network fault location based on improved sparrow algorithm

作　　者：王攀 胡业林[1] WANG Pan;HU Yelin(School of Electrical and Information Enigieering,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《哈尔滨商业大学学报（自然科学版）》2024年第3期307-314,共8页Journal of Harbin University of Commerce：Natural Sciences Edition

摘　　要：针对配电网故障定位准确率低、容错率低的问题,提出了一种基于改进麻雀搜索算法的配电网故障区段定位方法.在麻雀算法中引入动态自适应权重,加快麻雀算法的收敛速度,增强其局部开发和全局搜索的能力.通过在种群初始迭代中增加聚集阈值,提高算法的寻优精度.通过对单一故障场景、多故障场景下进行仿真测试验证改进麻雀算法的准确性和容错性,在IEEE33节点场景下,将改进后的麻雀搜索算法与粒子群算法、蛇算法、麻雀算法进行了比较,验证了改进后的麻雀算法的优越性,能够准确快速地实现故障定位.结果表明,改进后的麻雀搜索算法寻优能力更强,容错性更高.To address the issues of low accuracy and fault tolerance in distribution network fault location,an improved sparrow search algorithm based on distribution network fault location is proposed.Dynamic adaptive weights were introduced into the sparrow algorithm to accelerate its convergence speed and enhance its ability of local development and global search.By increasing the aggregation threshold in the initial iteration of the population,the optimization accuracy of the algorithm was improved.The accuracy and fault tolerance of the improved sparrow algorithm were verified by simulation tests under single fault scenario and multi-fault scenario.In the IEEE33-node scenario,the improved sparrow search algorithm was compared with particle swarm optimization algorithm,snake algorithm and sparrow algorithm,and the superiority of the improved sparrow algorithm was verified,which could accurately and quickly achieve fault location.The results showed that the improved sparrow search algorithm has stronger optimization ability and higher fault tolerance.

关 键 词：配电网 故障定位 麻雀搜索算法 信息畸变 信息缺失 自适应权重 聚集阈值 

分 类 号：TM726[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]