Partial Discharge Data Augmentation Based on Improved Wasserstein Generative Adversarial Network With Gradient Penalty
Guangya Zhu, Kai Zhou, Lu Lu, Yao Fu, Zhaogui Liu, Xiaomin Yang
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 article 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. First, 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 dataset. Finally, the expanded dataset is trained to get a new PD classifier. Experimental results demonstrate that the proposed model can generate new high-quality data samples more stably. Moreover, the proposed method can suppress the overfitting risk caused by low data or imbalanced data distributions and the classification accuracy is effectively improved.