Grounding Fault Diagnosis of Running Rails Based on a Multi-scale One-Dimensional Convolutional Neural Network in a DC Subway System

Abstract

Running rails are the return conductors of the traction current in DC subway systems, which should be insulated from the earth. Due to the large length of the line, as well as the humidity and metal dust in the tunnel, grounding fault of the running rails usually occurs, which will increase stray current (SC) leakage and endanger power supply safety. In this paper, a method of grounding fault diagnosis of running rails based on a multi-scale one-dimensional convolutional neural network (MS-1DCNN) is proposed. Firstly, a platform for the dynamic distribution of SC and rail potential (RP) with grounding faults existing in the running rails is established, which generates the dynamic RP data with various grounding faults. Secondly, a grounding fault diagnostic method of running rails based on MS-1DCNN is proposed, so as to realize the effective identification of ground fault types in subway systems. Thirdly, with the proposed diagnostic method, the datasets under two operating conditions of a single train and two trains are tested; a comparison test between MS-1DCNN and the 1D convolutional neural network (1DCNN) is carried out, and the effectiveness of the proposed method is verified. Results demonstrate that the proposed model can significantly improve the ground fault diagnostic accuracy of running rails. The dynamic RP simulation platform for trains established in this paper lays a theoretical foundation for the grounding fault research of running rail. Moreover, the deep learning method is proposed for the first time to diagnose the grounding fault of running rail, and the high diagnostic accuracy is obtained, which is of great significance for the safe and stable operation of the subway line.