A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

This paper proposes a semi-supervised autoencoder with an auxiliary task (SAAT) to extract a health feature space for power transformer fault diagnosis using dissolved gas analysis (DGA). The health feature space generated by a semi-supervised autoencoder (SSAE) not only identifies normal and therma...

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Bibliographic Details
Main Authors: Sunuwe Kim, Soo-Ho Jo, Wongon Kim, Jongmin Park, Jingyo Jeong, Yeongmin Han, Daeil Kim, Byeng Dong Youn
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Semi-supervised autoencoder
health feature space
fault diagnosis
power transformer
dissolved gas analysis
Online Access:https://ieeexplore.ieee.org/document/9210082/

Internet

https://ieeexplore.ieee.org/document/9210082/

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