Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis

Abstract

Accurately diagnosing various winding deformation faults is significant in power transformer maintenance. Among the different fault diagnosis methods, frequency response analysis (FRA) is widely used but still poses challenges. Artificial intelligence (AI)-based methods have recently been proposed to interpret FRA data. Nevertheless, these approaches are either complicated or exhibit limited generalization performance due to real-world FRA fault data scarcity. Inspired by AI-generated content (AIGC), this study proposes a data augmentation technique named conditional Wasserstein generative adversarial network with gradient penalty (Conditional-WGAN-GP) combined with fault diagnosis model. Numerous FRA-based data are automatically generated using the proposed data augmentation technique based on real FRA data obtained from a specially designed 10 kV transformer. The augmented dataset is then used to train fault diagnosis models to detect winding deformation faults. The trained fault diagnosis model is subsequently applied to assess two actual transformers. Experimental results demonstrate that when combined with the proposed method, even simpler fault diagnosis models can achieve high accuracy, exhibiting an improvement of approximately 5 % compared to the previous baseline model. The fault diagnosis models combined with the proposed data augmentation technique demonstrate improved generalization and robustness. (GitHub code: https://github.com/cy1034429432/Diagnosing-Transformer-Winding-Deformation-Fault-Types-from-FRA-Based-on-Conditional-WGAN-GP-/tree/main).