A Real-Time Fault Diagnosis Method for Grid-Side Overcurrent in Train Traction System Using Signal Time Series Feature Pattern Recognition

Abstract

Grid-side overcurrent (GSOC) is a common anomaly state in traction drive system (TDS). Once GSOC occurs, to protect the train, traction control units (TCU) will trip the main circuit breaker and stop the train in a short time. Since TCU cannot locate the source of the GSOC fault, troubleshooting can only be done by inspecting the components one by one after the train stops, which greatly affects the availability and maintenance efficiency of train. Therefore, this paper proposes a real-time fault diagnosis method based on time series feature pattern recognition of signals in TCU. Firstly, the time series pattern of GSOC caused by different fault sources are established. Secondly, the feature extraction method of time series pattern is carried out based on the fusion of engineering knowledge, variational mode decomposition and signal selection. Thirdly, the time series feature patterns of GSOC are identified to determine the most likely fault type of GSOC by Gaussians mixture model-Hidden Markov model (GMM-HMM). Finally, the proposed real-time fault diagnosis method is verified by the field test data. The results show that the proposed algorithm can realize real-time diagnosis of GSOC and trace the fault source effectively.