Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

Abstract This paper presents an advanced technique based on cross‐Stockwell transform (XST) and sparse autoencoder to predict the surface contamination severity of metal oxide surge arrester (MOSA) employing leakage current signal. Generally, MOSAs in power system network are exposed to different en...

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Bibliographic Details
Main Authors: Arup Kumar Das, Sovan Dalai, Biswendu Chatterjee
Format: Article
Language:English
Published: Wiley 2021-06-01
Series:IET Science, Measurement & Technology
Subjects:
Mathematical analysis
Signal processing and detection
Computer vision and image processing techniques
Integral transforms
Other topics in statistics
Current measurement
Online Access:https://doi.org/10.1049/smt2.12039

Internet

https://doi.org/10.1049/smt2.12039

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