A Hybrid Algorithm for Parameter Identification of Synchronous Reluctance Machines

Huan Wang, Guobin Lin (), Yuanzhe Zhao (), Sizhe Ren and Fuchuan Duan
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Huan Wang: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
Guobin Lin: National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
Yuanzhe Zhao: Institute of Rail Transit, Tongji University, Shanghai 201804, China
Sizhe Ren: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
Fuchuan Duan: College of Transportation Engineering, Tongji University, Shanghai 201804, China

Sustainability, 2022, vol. 15, issue 1, 1-19

Abstract: In rail transit traction, synchronous reluctance machines (SynRMs) are potential alternatives to traditional AC motors due to their energy-saving and low-cost characteristics. However, the nonlinearities of SynRMs are more severe than permanent magnet synchronous motors (PMSM) and induction motors (IM), which means the characteristics of SynRMs are challenging to model accurately. The parameter identification directly influences the modeling of nonlinearity, while the existing algorithms tend to converge prematurely. To overcome this problem, in this paper, a hybrid optimizer combining the SCA with the SSO algorithm is proposed to obtain the parameters of SynRMs, and the proposed Sine-Cosine self-adaptive synergistic optimization (SCSSO) algorithm preserves the self-adaptive characteristic of SSO and the exploration ability of SCA. Comprehensive numerical simulation and experimental tests have fully demonstrated that the proposed method has obviously improved parameter identification accuracy and robustness. In the dq -axis flux linkage, the mismatch between reference and estimated data of proposed algorithm is below 1% and 6%, respectively. Moreover, the best d-axis RMSE of SCSSO is 50% of the well-known algorithm CLPSO and 25% of BLPSO and its performance has improved by two orders of magnitude compared to traditional simple algorithms. In the q-axis, the best RMSE is 10% of CLPSO and 50% of Rao-3 and Jaya. Moreover, the performance of the proposed algorithm has improved nearly 90 times compared to traditional simple algorithms.

Keywords: rail transit traction; synchronous reluctance machines; nonlinear magnetic model; parameters identification; optimization problem (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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