High Impedance Fault Diagnosis Method Based on Conditional Wasserstein Generative Adversarial Network

Abstract

Data-driven fault diagnosis of high impedance fault (HIF) has received increasing attention and achieved fruitful results. However, HIF data is difficult to obtain in engineering. Furthermore, there exists an imbalance between the fault data and non-fault data, making data-driven methods hard to detect HIFs reliably under the small imbalanced sample condition. To solve this problem, this paper proposes a novel HIF diagnosis method based on conditional Wasserstein generative adversarial network (WCGAN). By adversarial training, WCGAN can generate sufficient labeled zero-sequence current signals, which can expand the limited training set and achieve the balanced distribution of the samples. In addition, the Wasserstein distance was introduced to improve the loss function. Experimental results indicate that the proposed method can generate high-quality samples and achieve a high accuracy rate of fault detection in the case of small imbalanced samples.