Propagation Characteristics and Identification of High-Order Harmonics of a Traction Power Supply System

High-order harmonics in the traction power supply show negative effects on the safe and stable operation of the railway transportation system. There is a fixed resonant frequency in the traction network. When the harmonic current frequency produced by the locomotive matches the resonant frequency of the traction network, it will cause high-frequency resonant overvoltage. The propagation path of the high-order harmonics of the traction load is analyzed based on a V/v wiring traction transformer. The propagation characteristics of high-order harmonics on self-used equipment at 380 V low-voltage side and 27.5 kV high-voltage side are expounded. A simulation model for the low-voltage self-consumption power system is established and the singular value decomposition algorithm is proposed to identify the harmonic impedance. The simulation results show that the proposed method can reduce the error to within 0.1%. Under realistic conditions, the overvoltage caused by high-order harmonics is difficult to identify. To solve this problem, an overvoltage identification algorithm for Electric Multiple Units based on a convolutional neural network is proposed. The ShuffleNet neural network model is then used to identify high-order harmonics overvoltage and other types of overvoltage. The overall accuracy of the proposed classification model can be improved from 97.12% to 98.44%. Better recognition and classification performances can also be achieved.