Identification of Arc Processes in the Contact Network of Urban Electric Transport by Methods of Oscillographic Monitoring and Spectral Processing

Objective: the research is aimed at developing effective methods for detecting arc processes in the DC contact network of ground-based urban electric transport that negatively affect the performance of the power supply system. Methods: the oscilloscope data of instantaneous values of currents and voltages recorded by emergency event devices is used. Spectral analysis and statistical processing algorithms are used to analyze the obtained waveforms, including artificial modeling of arc damage and subsequent verification of data from real traction substations. Results: algorithms for detecting arc processes have been developed, which have been confirmed experimentally and by analyzing data arrays recorded at three substations. Optimal levels of settings for recording arc events have been established (the average value is about 20 A), the sensitivity features of the algorithms and the need for further calibration of the parameters have been identified. simulation of arc damage and subsequent verification of data from real traction substations. Practical importance: the results obtained will improve the reliability of the urban electric transport power supply system by timely identifying and eliminating the negative effects of arc processes. The proposed method can be implemented into existing monitoring and diagnostic systems, reducing the risks of electrical equipment failure and improving the overall efficiency of transport networks.