Partial Discharge Data Augmentation Based on Improved Wasserstein Generative Adversarial Network With Gradient Penalty

Abstract

The partial discharge (PD) classification for electric power equipment based on machine learning algorithms often leads to insufficient generalization ability and low recognition accuracy. To solve the problem, this paper develops an improved Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based data augmentation model. The improved WGAN-GP model can generate data samples to supplement the low-data input set in PD source classification. Firstly, an improved WGAN-GP model with conditional generation is trained and various new data samples are generated. Then the new data samples are utilized to expand the raw data set. Finally, the expanded data set is trained to get a new PD classifier. Experimental results demonstrate the proposed model can generate new high-quality data samples more stably. Moreover, the proposed method can suppress the over-fitting risk caused by low-data or imbalanced data distributions and the classification accuracy is effectively improved.