A Convolutional neural network-based deep learning methodology for recognition of partial discharge patterns from high-voltage cables

IEEE Transactions on Power Delivery

Volumn 34, Issue 4, 2019, Pages 1460-1469

  Peng, Xiaosheng ^a   Yang, Fan ^a   Wang, Ganjun ^b   Wu, Yijiang ^b   Li, Lee ^a   Li, Zhaohui ^a   Bhatti, Ashfaque Ahmed ^a   Zhou, Chengke ^c   Hepburn, Donald M ^c   Reid, Alistair J ^d   Judd, Martin D ^e   Siew, Wah Hoon ^f  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b GUANGDONG POWER GRID CORPORATION   (China)

^c GLASGOW CALEDONIAN UNIVERSITY   (United Kingdom)

^d CARDIFF UNIVERSITY   (United Kingdom)

^e HIGH FREQUENCY DIAGNOSTICS AND ENGINEERING LTD   (United Kingdom)

^f UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

Convolutional neural network; deep learning; high voltage cables; partial discharge; pattern recognition

Indexed keywords

BACKPROPAGATION; CABLES; CONVOLUTION; DEEP LEARNING; DEEP NEURAL NETWORKS; DEFECTS; ETHYLENE; NETWORK LAYERS; NEURAL NETWORKS; PARTIAL DISCHARGES; RUBBER; SUPPORT VECTOR MACHINES;

BACK-PROPAGATION NEURAL NETWORKS; CONVOLUTIONAL KERNEL; CONVOLUTIONAL NEURAL NETWORK; ETHYLENE PROPYLENE RUBBER CABLES; HIGH VOLTAGE CABLE; HIGH VOLTAGE LABORATORIES; PARTIAL DISCHARGE PATTERN; PATTERN RECOGNITION ACCURACIES;

PATTERN RECOGNITION;

EID: 85069926419     PISSN: 08858977     EISSN: 19374208     Source Type: Journal    
DOI: 10.1109/TPWRD.2019.2906086     Document Type: Article

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