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author:

Lin, Jianxin (Lin, Jianxin.) [1] | Lin, Xiwen (Lin, Xiwen.) [2] | Wang, Huaiyuan (Wang, Huaiyuan.) [3] | Guo, Moufa (Guo, Moufa.) [4]

Indexed by:

EI

Abstract:

High impedance faults (HIFs) are difficult to detect because of low amplitude of the current signal. The interference from switching cases also jeopardizes the reliability of HIF detection (HIFD). Moreover, a long detection time is more likely to cause accidents. To diagnose HIFs promptly and precisely, a time-adaptive (TA) HIFD model is proposed. Firstly, the zero-sequence current data of the faulty feeder are processed into a variable-length training set to train gated recurrent unit (GRU) networks. Secondly, a cost-sensitive method is employed to train two biased GRU models with contrary preference. Then the two models are combined into a high reliability evaluation model. The predicted reliability depends on the consistency of predicted results of the two models. Reliable results are output, while the unreliable results are set aside. To prevent untimely detection, an equal GRU-based model is activated when reaching the time threshold. Delayed judgment improves accuracy of HIFD and reduces probability of harm caused by untimely detection. The performance of proposed method is validated by simulated data, and tested in a realistic 10 kV distribution network experimental system. In the true-type 10kV system, the TA HIFD model can achieve an accuracy of 96.73% with average detection time of 4.315ms. © 2025

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Community:

  • [ 1 ] [Lin, Jianxin]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Lin, Jianxin]College of Zhicheng, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Lin, Xiwen]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Wang, Huaiyuan]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 5 ] [Guo, Moufa]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

Reprint 's Address:

  • [wang, huaiyuan]college of electrical engineering and automation, fuzhou university, fuzhou; 350108, china

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Source :

Electric Power Systems Research

ISSN: 0378-7796

Year: 2025

Volume: 247

3 . 3 0 0

JCR@2023

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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