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 on test set data, making it difficult to accurately diagnose transformer faults. This paper uses a conditional Wasserstein conditional Wasserstein generative adversarial network with gradient penalty based on gradient penalty (CWGAN-GP) optimization to expand the sample size of transformer oil chromatography data containing a total of 500 sets of samples from 5 fault types. 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%.