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

Miaoxin Jin, Yuehuan Yang, Jiapeng Yang, Mingli Wu, Ganghui Xie and Kejian Song
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Miaoxin Jin: The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, CRRC Zhuzhou Locomotive Co., Ltd. (CRRC ZELC), Zhuzhou 412001, China
Yuehuan Yang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Jiapeng Yang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Mingli Wu: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Ganghui Xie: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Kejian Song: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China

Energies, 2022, vol. 15, issue 15, 1-15

Abstract: 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.

Keywords: railway electrification system; higher-order harmonics; resonance; overvoltage; light-weight convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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