A Data-Enhanced High Impedance Fault Detection Method Under Imbalanced Sample Scenarios in Distribution Networks

It is still a huge challenge for data-driven methods to detect high impedance fault (HIF) within limited field fault data. Even more, classification of imbalanced data has encountered a significant degradation in predictive performance since most classifiers were trained with the premise of a relatively balanced distribution. In this paper, a data-driven methodology combined with generative adversarial network (GAN) for HIF detection with imbalanced sample scenarios is proposed. By extending the structure and specializing loss function, the improved GAN (IGAN) can be established to learn the intrinsical probability distribution of raw data and sample new HIF data easily. Adding the high-quality dummy fault data into the original imbalanced training data set, equitable predictive accuracy can be achieved and unbiased classification models are available. Experimental results revealed that the proposed method can generate data subject to target distribution outperformed existing methods. Moreover, the validation results on filed data revealed that the proposed method can detect HIF at around 60 ms with fairly higher accuracy on imbalanced samples compared to mainstream data-driven algorithms.