Data Augmentation for Power Factor Correction Fault Classification: A GANs Approach

Abstract

The growing adoption of electric vehicles (EVs) has heightened the need for reliable and efficient On-Board Chargers (OBCs). Power Factor Correction (PFC) circuits within OBCs are critical for optimizing energy conversion and minimizing power losses. However, fault diagnosis in PFC circuits remains a challenge due to the difficulty of replicating real-world fault scenarios for data collection. This study addresses these challenges by employing Generative Adversarial Networks (GANs) to augment fault signal data. By generating diverse and realistic fault signals, this approach enhances the robustness of fault classification models. The proposed CRNNWGAN model, a fusion of C-RNN-GAN and WGAN-GP, effectively captures temporal dependencies and improves the accuracy of fault diagnosis. Experimental results demonstrate the superiority of the augmented dataset in classification tasks, providing a scalable solution for improving the reliability of EV charging systems.