Data-driven model for direct-supply traction network loss estimation

Xiaoman Zhao; Shaobing Yang

doi:10.1117/12.2680815

23 May 2023 Data-driven model for direct-supply traction network loss estimation

Xiaoman Zhao, Shaobing Yang

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264522 (2023) https://doi.org/10.1117/12.2680815
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Traction network loss is an important index to evaluate the economy and safety of electrified railway operation and management and is of great significance to evaluate the transportation capacity of the line and the power supply capacity of the traction power supply system. In this paper, the equivalent circuit model for traction network parameter identification is first studied, and the formula of traction network loss measurement is derived. Next, a multidimensional impedance database model is established for storing the equivalent impedance. Based on the derived formula and the established model, the feasibility and effectiveness of applying the measurement data from the currently deployed measurement system to the method in this paper are analyzed. The results show that the loss estimation error meets the actual engineering needs.

Citation Download Citation

Xiaoman Zhao and Shaobing Yang "Data-driven model for direct-supply traction network loss estimation", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264522 (23 May 2023); https://doi.org/10.1117/12.2680815

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