A Novel Fault Location Method for Power Cables Based on an Unsupervised Learning Algorithm

Mingzhen Li, Jialong Bu, Yupeng Song, Zhongyi Pu, Yuli Wang and Cheng Xie
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Mingzhen Li: School of Electrical Engineering, Nantong University, No. 9, Seyuan Road, Nantong 226019, China
Jialong Bu: School of Electrical Engineering, Nantong University, No. 9, Seyuan Road, Nantong 226019, China
Yupeng Song: School of Electrical Engineering, Nantong University, No. 9, Seyuan Road, Nantong 226019, China
Zhongyi Pu: School of Electrical Engineering, Nantong University, No. 9, Seyuan Road, Nantong 226019, China
Yuli Wang: China Electric Power Research Institute, Wuhan 430074, China
Cheng Xie: Zhejiang Electric Power Research Institute, Hangzhou 310014, China

Energies, 2021, vol. 14, issue 4, 1-19

Abstract: In order to locate the short-circuit fault in power cable systems accurately and in a timely manner, a novel fault location method based on traveling waves is proposed, which has been improved by unsupervised learning algorithms. There are three main steps of the method: (1) build a matrix of the traveling waves associated with the sheath currents of the cables; (2) cluster the data in the matrix according to its density level and the stability, using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN); (3) search for the characteristic cluster point(s) of the two branch clusters with the smallest density level to identify the arrival time of the traveling wave. The main improvement is that high-dimensional data can be directly used for the clustering, making the method more effective and accurate. A Power System Computer Aided Design (PSCAD) simulation has been carried out for typical power cable circuits. The results indicate that the hierarchical structure of the condensed cluster tree corresponds exactly to the location relationship between the fault point and the monitoring point. The proposed method can be used for the identification of the arrival time of the traveling wave.

Keywords: power cable; fault location; sheath current; traveling wave; unsupervised learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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