SONG Hui, YUAN Longxiang, GUO Shuangquan. GWO-ResNet Power Transformer Fault Diagnosis Method Based on Data Augmentation and Feature Attention Mechanism[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2022.0163
Citation: SONG Hui, YUAN Longxiang, GUO Shuangquan. GWO-ResNet Power Transformer Fault Diagnosis Method Based on Data Augmentation and Feature Attention Mechanism[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2022.0163

GWO-ResNet Power Transformer Fault Diagnosis Method Based on Data Augmentation and Feature Attention Mechanism

  • To improve the accuracy of transformer fault diagnosis, this paper proposes a GWO-ResNet fault diagnosis method based on data augmentation and feature attention mechanism. Aimed at the effect produced by imbalanced dataset on the power transformer fault diagnosis model, the Wasserstein generative adversarial network with gradient penalty (WGANGP) is utilized to make data augmentation for the power transformer dataset. Secondly, to enhance the sensitivity of the model to the key features in the augmented dataset, a feature attention mechanism is introduced into the input side of the model. Thirdly, in order to accelerate the convergence of the model, the grey wolf optimization algorithm (GWO) was used to optimize the residual neural network(ResNet) in the early stage of training. Finally, the validity of the proposed WGANGP-ATT-GWO-ResNet fault diagnosis model is verified based on a measured power transformer dataset.
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