A Novel Double-Stacked Autoencoder for Power Transformers DGA Signals With An Imbalanced Data Structure

Artificial intelligence is the general trend in the field of power equipment fault diagnosis. However, limited by operation characteristics and data defects, the application of the intelligent diagnosis method in power transformers is still in the initial stage. To fill this research gap, in this ar...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 69; no. 2; pp. 1977 - 1987
Main Authors: Yang, Dongsheng, Qin, Jia, Pang, Yongheng, Huang, Tingwen
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aging
Artificial intelligence
Clustering
Data structures
Fault diagnosis
Feature extraction
Imbalanced data
intelligent fault diagnosis
Loss measurement
Oil insulation
Power transformer insulation
power transformers
stacked autoencoder (SAE)
Training
Transformers
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary:Artificial intelligence is the general trend in the field of power equipment fault diagnosis. However, limited by operation characteristics and data defects, the application of the intelligent diagnosis method in power transformers is still in the initial stage. To fill this research gap, in this article, a novel double-stacked autoencoder (DSAE) is proposed for a fast and accurate judgment of power transformer health conditions with an imbalanced data structure. Three problems affecting the diagnosis effectiveness are overcome by a DSAE framework, an aging-tolerance criterion, and an advanced sparse deep clustering network. The proposed DSAE method is validated by two case studies based on an actual power transformer dataset. The results indicate that the proposed DSAE method can achieve a fairly reliable diagnosis with a higher accuracy and less time than the other methods, which demonstrates the superiority and effectiveness of the proposed approach.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3059543