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Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

Tusongjiang. Kari1, Lin Du1, Aisikaer. Rouzi2, Xiaojing Ma1,*, Zhichao Liu1, Bo Li1

1 School of Electrical Engineering, Xinjiang University, Urumqi, 830047, China
2 School of Mathematics, Xinjiang University, Urumqi, 830047, China

* Corresponding Author: Xiaojing Ma. Email: email

Computers, Materials & Continua 2023, 75(2), 4573-4592. https://doi.org/10.32604/cmc.2023.037954

Abstract

The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long short-term memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis. The discriminator adopts multilayer perceptron networks (MLP), which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large. The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models, enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%. Furthermore, the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods. Therefore, the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets. In addition, the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

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APA Style
Kari, T., Du, L., Rouzi, A., Ma, X., Liu, Z. et al. (2023). Fault diagnosis of power transformer based on improved ACGAN under imbalanced data. Computers, Materials & Continua, 75(2), 4573-4592. https://doi.org/10.32604/cmc.2023.037954
Vancouver Style
Kari T, Du L, Rouzi A, Ma X, Liu Z, Li B. Fault diagnosis of power transformer based on improved ACGAN under imbalanced data. Comput Mater Contin. 2023;75(2):4573-4592 https://doi.org/10.32604/cmc.2023.037954
IEEE Style
T. Kari, L. Du, A. Rouzi, X. Ma, Z. Liu, and B. Li "Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data," Comput. Mater. Contin., vol. 75, no. 2, pp. 4573-4592. 2023. https://doi.org/10.32604/cmc.2023.037954



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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