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

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

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
- Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems
- 中国电机工程学会电力与能源系统学报(英文) 2023年9卷第2期页码：599-611
- 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
  中图分类号：
- 纸质出版：2023-03-01
- 稿件说明：
移动端阅览
Ye Tian, Mou-Fa Guo, Duan-Yu Chen. Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(2):599-611.

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.
Ye Tian, Mou-Fa Guo, Duan-Yu Chen. Intermittent Arc Fault Detection Based on Machine Learning in Resonant Grounding Distribution Systems[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(2):599-611. DOI： 10.17775/CSEEJPES.2019.03410.

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.

关键词

Keywords

references

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Enhancing Information Sensing of Load Forecasting in Cyber-Physical-Social Systems: An Approach with Large Language Model

Short-term load forecasting of regional distribution network based on generalized regression neural network optimized by grey wolf optimization algorithm

Fault line detection using waveform fusion and one-dimensional convolutional neural network in resonant grounding distribution systems

A deep neural network coordination model for electric heating and cooling loads based on IoT data

Temporal Convolutional Auto-Encoder for Residential Non-Intrusive Load Monitoring