Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems

Ye Tian; Mou-Fa Guo; Duan-Yu Chen

doi:10.17775/CSEEJPES.2019.03410

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Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems

Regular Papers | 更新时间：2026-10-15

- Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems
- CSEE Journal of Power and Energy Systems Vol. 9, Issue 2, Pages: 599-611(2023)
- 作者机构：
  
  1. College of Electrical Engineering and Automation, Fuzhou University,Fuzhou,China
  2. Department of Electrical Engineering, Yuan Ze University,Taiwan,China
  3. Fuzhou Power Supply Company of State Grid Fujian Electric Power Co., Ltd.,Fuzhou,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.03410
  CLC：
- Published：01 March 2023
- 稿件说明：
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Ye Tian, Mou-Fa Guo, Duan-Yu Chen. Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems[J]. CSEE Journal of Power and Energy Systems, 2023, 9(2): 599-611.
DOI：

Ye Tian, Mou-Fa Guo, Duan-Yu Chen. Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems[J]. CSEE Journal of Power and Energy Systems, 2023, 9(2): 599-611. DOI： 10.17775/CSEEJPES.2019.03410.

摘要

Abstract

In resonant grounding systems

most single-phase-to-ground faults evolve from IAFs (Intermittent Arc Faults). Earlier detection of IAFs can facilitate fault avoidance. This work proposes a novel method based on machine learning for detecting IAFs in three steps. First

the feature of zero-sequence current is automatically extracted and selected by a newly-designed FINET (“For IAFs

Neuron Elaboration Net”)

instead of traditional feature selection based on time-frequency decomposition. Moreover

data of the zero-sequence current divided by different time windows are successively input into the trained FINET. A proposed PSF (principal-subordinate factor) analyses the results obtained from FINET to improve anti-interference in the mentioned IAF detection algorithm. Experiments using PSCAD/EMTDC software simulation data show the proposed method is feasible and highly adaptable. In addition

the detection result of on-site recorded data demonstrates the effectiveness of the proposed method in practical resonant grounding systems.

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