Research on Modeling and Power Supply Capability of AT Traction Power Supply System

In the development process of large-scale railway transportation system, there are various line structures, resulting in drastic fluctuations of traction power supply system, which brings great difficulties to ensure the power supply capacity of traction power supply system. Traditional power flow c...

Full description

Saved in:
Bibliographic Details
Published in2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) pp. 186 - 189
Main Authors Fan, Zixu, Tian, Xingjun, Sun, Xuelei, Yang, Xiaoxuan, Zhang, Hao, Tan, Zihang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2021
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:In the development process of large-scale railway transportation system, there are various line structures, resulting in drastic fluctuations of traction power supply system, which brings great difficulties to ensure the power supply capacity of traction power supply system. Traditional power flow calculation methods, such as N-R method and PQ decomposition method, can only calculate highly linearized direct power supply system, and are no longer applicable to new power supply systems. Therefore, it is necessary to study a suitable method to analyze the power supply capacity of AT traction power supply system effectively and accurately. In this paper, firstly, the AT traction power supply system model is built, including substation, substation and AT substation. Then, according to the basic parameters and operation characteristics of electric locomotive, the equivalent circuit model of electric locomotive is built to realize the complete restoration of traction power supply system. Then, the power flow of traction power supply system under different working conditions is calculated, and the power supply capacity under single-vehicle light load, single-vehicle heavy load, multi-vehicle light load and multi-vehicle heavy load is explored. Finally, the model is used to verify it again, and a substantial conclusion is obtained.
DOI:10.1109/AIEA53260.2021.00047