GAN-based semi-supervised learning method for identification of the faulty feeder in resonant grounding distribution networks

Accurate identification of the faulty feeder after a single-phase-to-ground fault in an arc suppression coil grounding distribution network remains a significant challenge. Recently, intelligent learning-based methods and especially deep learning methods have attracted much attention. To our knowledge, these methods usually assume that the training samples are sufficient. However, it is difficult to obtain abundant labeled samples in a practical environment. In this paper, we propose a semi-supervised learning model based on generative adversarial networks (GAN) to reliably identify the faulty feeder. The discriminator of GAN classifies the synthetic data which is generated by the generator into a new class. We revise the loss functions of the discriminator and generator, and the discriminator and generator play a maximum and minimum game. The proposed method can significantly improve the performance of faulty feeder identification when a small percentage of training samples are labeled. Various types of faults are simulated to consist of the training dataset and testing dataset. The simulation test results, sensitive analysis, comparing analysis, and recorded data obtained in a full-scale test field test demonstrate the higher performance of the proposed method.