High-Impedance Fault Detection Methodology Using Time–Frequency Spectrum and Transfer Convolutional Neural Network in Distribution Networks-Reference-Cited by-同舟云学术

High-Impedance Fault Detection Methodology Using Time–Frequency Spectrum and Transfer Convolutional Neural Network in Distribution Networks

Published:2023-09 Issue:3 Volume:17 Page:4002-4013
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Guo Mou-Fa¹^ORCID,Guo Zi-Yi¹^ORCID,Gao Jian-Hong¹^ORCID,Chen Duan-Yu²^ORCID

Affiliation:

1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

2. Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/10235272/10153959.pdf?arnumber=10153959

Reference35 articles.

1. A Resilient Protection Scheme for Common Shunt Fault and High Impedance Fault in Distribution Lines Using Wavelet Transform

2. Regulatory impact statement: Bushfire mitigation regulations ammendment. department of economic development,2015

3. High Impedance Arc Fault Detection Based on the Harmonic Randomness and Waveform Distortion in the Distribution System

4. Sustainable Deep Learning at Grid Edge for Real-time High Impedance Fault Detection

5. High Impedance Fault detection based on harmonic energy variation via S-transform

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