Traction Load Modeling and Parameter Identification Based on Improved Sparrow Search Algorithm

Zhensheng Wu, Deling Fan and Fan Zou
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Zhensheng Wu: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Deling Fan: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Fan Zou: Department of Earth Science, Uppsala University, 62157 Visby, Sweden

Energies, 2022, vol. 15, issue 14, 1-18

Abstract: In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor’s dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

Keywords: improved sparrow search algorithm; parameter identification; traction load; load modeling (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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