Positional Encoding Application and Analysis Based on Transformer Generative Adversarial Network for Power Factor Correction Fault Diagnosis Data Augmentation

Power Factor Correction (PFC) circuits play a vital role in improving power quality and ensuring the stability of power systems. However, collecting real-world fault data for these circuits is costly and time-consuming, making it difficult to train reliable diagnostic models. To address this issue, this study proposes a data augmentation method using a Transformer-based Generative Adversarial Network(GAN) integrated with Positional Encoding. The proposed approach captures the temporal dependencies and nonlinear characteristics of PFC fault signals more effectively than traditional techniques. Experimental evaluations using t-SNE, Maximum Mean Discrepancy(MMD), and multiple classification models confirm the advancement of the proposed method in generating realistic and diverse fault data. This research contributes to enhancing the robustness and accuracy of fault diagnosis models and offers scalability to other power electronic systems.