电气技术2024，Vol.25Issue(3)：24-31,37,9.
基于K均值聚类算法的谐振接地系统故障区段定位方法
Fault section location method in resonant grounding systems based on K-means clustering algorithm
摘要
Abstract
The current fault location method for resonant grounding distribution networks faces challenges such as excessive communication dependence,complex feature analysis,and difficulties in setting thresholds,resulting in reduced applicability for on-site operation.By studying the characteristics of three-phase current waveform variations,this paper,based on the deep integration of intelligent switches in distribution networks,introduces a local section selection method using the K-means clustering algorithm.This method extracts fault feature parameters,combining the advantages of unsupervised learning through the K-means clustering algorithm to identify section types.This approach allows each detection node to process only local fault signals,reducing the communication burden.The feasibility of this method is validated using both simulation and on-site data.Experimental results demonstrate that this method exhibits high reliability across various fault conditions and can effectively adapt to real-world environments.关键词
谐振接地系统/单相接地故障/就地选段/K均值聚类/非监督学习Key words
resonant grounding system/single line to ground fault/local section selection/K-means clustering/unsupervised learning引用本文复制引用
黄劼,汪逸帆,林叶青,胡荔丹,王丹豪..基于K均值聚类算法的谐振接地系统故障区段定位方法[J].电气技术,2024,25(3):24-31,37,9.基金项目
国网福建省电力有限公司科学技术项目(521310230004) （521310230004）
