A Grounding Fault Location Method for Auxiliary Power Supply Circuit in Electrical Locomotive

Accurately and quickly identifying and locating grounding faults (GFs) in auxiliary power supply system (APS) of electrical locomotives (ELs) are crucial for improving the intelligence level and maintenance efficiency of EL. However, existing methods that can only detect abnormal grounding conditions and still require manual troubleshooting of GFs. Therefore, in this article, a waveform feature learning based GF diagnosis method is proposed by combining fault mechanisms with data learning. First, the GF mechanisms are studied and the related relationships between detecting grounding voltage and system signals under different GF locations are investigated. Second, two feature variables are constructed based on the selected correlation signals and feature indicators are extracted to increase the discrimination of waveform characteristics for different GF locations. Then, a classification learning based diagnosis framework is developed to tracing fault locations. Finally, the proposed method is validated by the experimental platform that the running status of APS is simulated based on hardware in the loop (HIL) and real-time diagnostic programs are executed on actual diagnostic processing board. The experimental results show that this method can detect and accurately locate ground faults in real time.