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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Vydáno v:	IEEE access Ročník 8; s. 178295 - 178310
Hlavní autoři:	Kim, Sunuwe, Jo, Soo-Ho, Kim, Wongon, Park, Jongmin, Jeong, Jingyo, Han, Yeongmin, Kim, Daeil, Youn, Byeng Dong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Comparative studies Decoding Deep learning Degradation dissolved gas analysis Dissolved gases Electrical faults Fault diagnosis Feature extraction Gas analysis health feature space Machine learning Performance degradation power transformer Power transformers Semi-supervised autoencoder Task analysis Transformers Two dimensional displays Visualization
ISSN:	2169-3536, 2169-3536
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Abstract	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 thermal/electrical fault types, but also presents the underlying characteristics of DGA. In the proposed approach, by adding an auxiliary task that detects normal and fault states in the loss function of SSAE, the health feature space additionally enables visualization of health degradation properties. The overall procedure of the new approach includes three key steps: 1) preprocessing DGA data, 2) extracting two health features via SAAT, and 3) visualizing the two health features in two-dimensional space. In this paper, we test the proposed approach using massive unlabeled/labeled Korea Electric Power Corporation (KEPCO) databases and IEC TC 10 databases. To demonstrate the effectiveness of the proposed approach, four comparative studies are conducted with these datasets; the studies examined: 1) the effectiveness of an auxiliary detection task, 2) the effectiveness of the visualization method, 3) conventional fault diagnosis methods, and 4) the state-of-the-art, semi-supervised deep learning algorithms. By examining several evaluation metrics, these comparative studies confirm that the proposed approach outperforms SSAE without the auxiliary task, existing methods, and state-of-the-art deep learning algorithms, in terms of defining health degradation performance. We expect that the proposed SAAT-based health feature space approach will be widely applicable to intuitively monitor the health state of power transformers in the real world.
AbstractList	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 thermal/electrical fault types, but also presents the underlying characteristics of DGA. In the proposed approach, by adding an auxiliary task that detects normal and fault states in the loss function of SSAE, the health feature space additionally enables visualization of health degradation properties. The overall procedure of the new approach includes three key steps: 1) preprocessing DGA data, 2) extracting two health features via SAAT, and 3) visualizing the two health features in two-dimensional space. In this paper, we test the proposed approach using massive unlabeled/labeled Korea Electric Power Corporation (KEPCO) databases and IEC TC 10 databases. To demonstrate the effectiveness of the proposed approach, four comparative studies are conducted with these datasets; the studies examined: 1) the effectiveness of an auxiliary detection task, 2) the effectiveness of the visualization method, 3) conventional fault diagnosis methods, and 4) the state-of-the-art, semi-supervised deep learning algorithms. By examining several evaluation metrics, these comparative studies confirm that the proposed approach outperforms SSAE without the auxiliary task, existing methods, and state-of-the-art deep learning algorithms, in terms of defining health degradation performance. We expect that the proposed SAAT-based health feature space approach will be widely applicable to intuitively monitor the health state of power transformers in the real world.
Author	Kim, Wongon Youn, Byeng Dong Kim, Daeil Jo, Soo-Ho Jeong, Jingyo Han, Yeongmin Kim, Sunuwe Park, Jongmin
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SubjectTerms	Algorithms Comparative studies Decoding Deep learning Degradation dissolved gas analysis Dissolved gases Electrical faults Fault diagnosis Feature extraction Gas analysis health feature space Machine learning Performance degradation power transformer Power transformers Semi-supervised autoencoder Task analysis Transformers Two dimensional displays Visualization
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Title	A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis
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