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Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Xiuyong Yu Jun Cao Zhong Fan Mingming Xu Liye Xiao

中国电机工程学会电力与能源系统学报（英文版）2023，Vol.9Issue(6)：2168-2178,11.

中国电机工程学会电力与能源系统学报（英文版）2023，Vol.9Issue(6)：2168-2178,11.DOI:10.17775/CSEEJPES.2020.05450

Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Xiuyong Yu ¹Jun Cao ²Zhong Fan ²Mingming Xu ³Liye Xiao¹

作者信息

1. Institute of Electrical Engineering and the Key Laboratory of Applied Superconductivity,Chinese Academy of Sciences,Beijing 100190,China||University of Chinese Academy of Sciences(UCAS),Beijing 100049,China
2. Keele University,Keele,STS 5GB,UK
3. Electric Power Research Institute of State Grid Henan Electric Power Company,Zhengzhou 450000,China
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摘要

关键词

Deep-learning method/faulty feederc identification/full-scale test field(FSTF)/resonant groundingc distribution network/single line to ground fault/transfer learning

Key words

Deep-learning method/faulty feederc identification/full-scale test field(FSTF)/resonant groundingc distribution network/single line to ground fault/transfer learning

引用本文复制引用

Xiuyong Yu,Jun Cao,Zhong Fan,Mingming Xu,Liye Xiao..Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning[J].中国电机工程学会电力与能源系统学报（英文版）,2023,9(6):2168-2178,11.

基金项目

This work was supported in part by the Key Program of the Chinese Academy of Sciences under Grant QYZDJ-SSW-JSC025,and in part by the National Natural Science Foundation of China under Grant 51721005,and in part by the Chinese Scholarship Council(CSC). （CSC）

中国电机工程学会电力与能源系统学报（英文版）

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ISSN：2096-0042

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