Method to Detect Arc Across Pantograph-Catenary Structure Atop Train Based on Frequency Features of Entry Current

During a train running at speed, the pantograph-catenary structure atop the train would be influenced by many factors, resulting in disconnections. A timely detection on arcs across this structure helps to guide works on real-time operating and later maintenance, to eliminate latent factors of disconnection. A theory on the generation of arcs is researched, simulated, and verified with practical data. Two features of entry current, in frequency bands at kHz degree and at harmonic-Hz degree, are picked for the detection. Based on the discrepancy on these bands while arcing and normally running, a model of support vector machine (SVM) is constructed. Existing signals on train are processed and rearranged to be datasets for training SVM. After applying it to a practical railway, the results illustrate: the accuracy on arc detections is up to 99.96%. With guidance from results, an on-site investigation was launched, and mark of flaming was found at corresponding location of catenary, which verified the correctness.