PFC 고장진단 데이터 증강을 위한 Transformer GAN 기반 포지션 엔코딩 적용 및 분석

초록

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.

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