基于K均值聚类算法的谐振接地系统故障区段定位方法

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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.

Key words： resonant grounding system single line to ground fault local section selection K-means clustering unsupervised learning

Received: 14 November 2023

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	Articles by authors
	HUANG Jie
	WANG Yifan
	LIN Yeqing
	HU Lidan
	WANG Danhao

Cite this article:

HUANG Jie,WANG Yifan,LIN Yeqing等. Fault section location method in resonant grounding systems based on K-means clustering algorithm[J]. Electrical Engineering, 2024, 25(3): 24-31.

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http://dqjs.cesmedia.cn/EN/Y2024/V25/I3/24

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