Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder

Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results...

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Veröffentlicht in:Heliyon Jg. 10; H. 9; S. e30670
Hauptverfasser: Zhang, Lei, Xu, Zhongyang, Lu, Chen, Qiao, Tianjiao, Su, Hongzhi, Luo, Yazhou
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 15.05.2024
Elsevier
Schlagworte:
Adversarial neural networks
algorithms
Chromatographic data
chromatography
Fault data
neural networks
oils
prediction
Sample expansion
Transformer
Fault data
Sample expansion
Transformer
Chromatographic data
Adversarial neural networks
ISSN:2405-8440, 2405-8440
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Zusammenfassung:Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results on test set data, making it difficult to accurately diagnose transformer faults. A conditional Wasserstein generative adversarial network with gradient penalty optimization (CWGAN-GP) is adopted in this paper, which based on gradient penalty optimization and expand the oil chromatography fault samples of 500 sets of transformer oil chromatography data with 5 types of faults. The proposed method is used to classify transformer faults using a deep autoencoder, and the sample quality of the neural network model proposed in this paper is compared with several other variants of generative adversarial neural network models. The research results show that after using the method proposed in this paper for sample expansion, the overall accuracy of fault diagnosis can reach 93.2 %, which is 4.98 % higher than the original imbalanced samples. Compared with other sample expansion methods, the accuracy of fault diagnosis of the algorithm in this paper is improved by 1.70 %–3.05 %.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e30670