High-Impedance Fault Detection Methodology Using Time–Frequency Spectrum and Transfer Convolutional Neural Network in Distribution Networks

Abstract

High-impedance fault (HIF) detection has always been difficult in distribution networks due to the lack of field data and the large difference between field and simulation waveforms. Based on the characteristics of zero-sequence currents, a novel HIF detection methodology is proposed, which combines time–frequency spectrum (TFS) and transfer convolutional neural network (TCNN). First, the TFSs are acquired by applying continuous wavelet transform (CWT) to the collected zero-sequence currents. Then, the TFSs of simulated zero-sequence currents are utilized for training source-domain convolutional neural network (SCNN). Next, the SCNN is transfer learned with very few TFSs of field zero-sequence currents to obtain TCNN. The performance of the proposed method is verified by simulation samples and field samples. The results show that the proposed method can effectively extract fault features from small-scale training samples under different fault circumstances. Besides, TCNN can adaptively extract the effective features of field HIF and detect field HIF more accurately than SCNN. Finally, this article provides a visualization scheme for interpretability of the neural network, which offers visual explanations for the decision-making basis of the neural network.