GAN-based semi-supervised learning method for identification of the faulty feeder in resonant grounding distribution networks-Reference-Cited by-同舟云学术

GAN-based semi-supervised learning method for identification of the faulty feeder in resonant grounding distribution networks

Published:2023-01 Issue: Volume:144 Page:108535
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Yu Xiuyong,Gu Juping,Zhang Xinsong,Mao Jingfeng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference29 articles.

1. Multi-agent based coordinated protection systems for distribution feeder fault diagnosis and reconfiguration;Rahman;Int J Electr Power Energy Syst,2018

2. Single-line-to-ground fault feeder selection considering device polarity reverse installation in resonant grounding system;Liu;IEEE Trans Power Del,2021

3. Resonance analysis and faulty feeder identification of high-impedance faults in a resonant grounding system;Xue;IEEE Trans Power Del,2017

4. Novel protection scheme of single-phase earth fault for radial distribution systems with distributed generators;Wang;IEEE Trans Power Del,2018

5. Faulty feeder identification in resonant grounding distribution networks based on deep learning and transfer learning;Yu;CSEE J Power Energy Syst,2021

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3. Semi-supervised fault diagnosis of gearbox based on feature pre-extraction mechanism and improved generative adversarial networks under limited labeled samples and noise environment;Advanced Engineering Informatics;2023-10

4. Advances on fault detection techniques for resonant grounded power distribution networks in bushfire prone areas: Identification of faulty feeders, faulty phases, faulty sections, and fault locations;Electric Power Systems Research;2023-07