基于大数据分析的配电网低压故障定位研究  

Research on Low Voltage Fault Location of Distribution Network Based on Big Data Analysis

作　　者：劳永钊 许健 黄奕俊 肖健 吴任博 危国恩 LAO Yongzhao;XU Jian;HUANG Yijun;XIAO Jian;WU Renbo;WEI Guo’en(Guangzhou Power Supply Bureau,Guangzhou 510260,China;Weisheng Information Technology Co.,Ltd.,Changsha 410012,China)

机构地区：[1]广东电网有限责任公司广州供电局,广东广州510620 [2]威胜信息技术股份有限公司,湖南长沙410012

出　　处：《微型电脑应用》2024年第5期49-52,共4页Microcomputer Applications

基　　金：广州供电局(GZHKJXM20190058)。

摘　　要：为了维护配电网正常运行,以提升配电网低压故障定位能力为目标,提出基于大数据分析的配电网低压故障定位方法。通过采集配电网低压故障定位数据,利用大数据分析算法中的支持向量机算法进行分类,从而实现配电网低压故障定位。使用改进粒子群算法改进支持向量机算法,优化配电网低压故障定位结果,并进行配电网低压故障定位的仿真实验。结果表明,所提方法解决了配电网低压故障定位方法存在的弊端,获得了理想的配电网低压故障定位效果,能够满足配电网低压故障定位实际要求。In order to maintain the normal operation of the distribution network and improve the low voltage fault location ability of the distribution network,a low voltage fault location method based on big data analysis is proposed.By collecting low voltage fault location data in the distribution network and using the support vector machine algorithm in big data analysis algorithms for classification,low voltage fault location in the distribution network can be achieved.An improved particle swarm optimization algorithm to improve support vector machine algorithm to optimize low voltage fault location results in distribution network,and the simulation experiment is conducted to locate low voltage fault in the distribution network.The results show that the proposed method solves the drawbacks of low voltage fault location method in distribution network,achieves ideal low voltage fault location results,which can meet the practical requirements of low voltage fault location in distribution network.

关 键 词：大数据分析 支持向量机 配电网低压故障 分类器设计 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]