Identification of Partial Discharge Defects Based on Deep Learning Method

IEEE Transactions on Power Delivery

Volumn 34, Issue 4, 2019, Pages 1557-1568

  Duan, Lian ^a   Hu, Jun ^a   Zhao, Gen ^a   Chen, Kunjin ^a   He, Jinliang ^a   Wang, Shan X ^b  

^a TSINGHUA UNIVERSITY   (China)

^b STANFORD UNIVERSITY   (United States)

Author keywords

deep learning; feature extraction; Partial discharge; softmax; sparse autoencoder

Indexed keywords

CLASSIFICATION (OF INFORMATION); DEFECTS; FEATURE EXTRACTION; PARTIAL DISCHARGES; PRINCIPAL COMPONENT ANALYSIS; STOCHASTIC SYSTEMS;

ACTIVATION FUNCTIONS; AUTO ENCODERS; INSULATION BREAKDOWN; PARTIAL DISCHARGE DEFECTS; PRINCIPLE COMPONENT ANALYSIS; SOFTMAX; STOCHASTIC NEIGHBOR EMBEDDING; ULTRA-HIGH FREQUENCY;

DEEP LEARNING;

EID: 85069908746     PISSN: 08858977     EISSN: 19374208     Source Type: Journal    
DOI: 10.1109/TPWRD.2019.2910583     Document Type: Article

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