Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers-Reference-Cited by-同舟云学术

Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers

Published:2022 Issue:3 Volume:45 Page:262-271
ISSN:2694-1783
Container-title:IEEE Canadian Journal of Electrical and Computer Engineering
language:
Short-container-title:IEEE Can. J. Electr. Comput. Eng.

Author:

Guan Xiangyu¹^ORCID,Xue Shupeng¹,Peng Hui²,Shu Naiqiu²,Gao Wei³^ORCID,Gao David Wenzhong³^ORCID

Affiliation:

1. Fujian Key Laboratory of New Energy Generation and Power Conversion, School of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

2. School of Electrical Engineering and Automation, Wuhan University, Wuhan, China

3. Department of Electrical and Computer Engineering, University of Denver, Denver, CO, USA

Funder

Natural Science Foundation of Fujian Province

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

http://xplorestaging.ieee.org/ielx7/9349829/9944186/09791329.pdf?arnumber=9791329

Reference35 articles.

1. Finite Element Analysis on Forces, Currents, and Magnetic Fluxes of Misaligned GIS Disconnector

2. Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases

3. Convolutional Neural Networks for Electrical Endurance Prediction of Alternating Current Contactors

4. Machine vision inspection of electrical connectors based on improved YOLO V3;weihao;IEEE Access,2020

5. A simple way to initialize recurrent networks of rectified linear units;le;arXiv 1504 00941,2015

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