Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model

Linjie Ren, Guobin Lin, Yuanzhe Zhao and Zhiming Liao
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Linjie Ren: Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai 201804, China
Guobin Lin: National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
Yuanzhe Zhao: Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai 201804, China
Zhiming Liao: National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China

Sustainability, 2021, vol. 13, issue 8, 1-14

Abstract: In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in the parameter extraction of SynRM nonlinear magnetic model, the accuracy and robustness of the metaheuristic algorithm is restricted by the excessive dependence on fitness evaluation. In this paper, a novel probability-driven smart collaborative performance (SCP) is defined to quantify the comprehensive contribution of candidate solution in current population. With the quantitative results of SCP as feedback in-formation, an algorithm updating mechanism with improved evolutionary quality is established. The allocation of computing resources induced by SCP achieves a good balance between exploration and exploitation. Comprehensive experiment results demonstrate better effectiveness of SCP-induced algorithms to the proposed synchronous reluctance machine magnetic model. Accuracy and robustness of the proposed algorithms are ranked first in the comparison result statistics with other well-known algorithms.

Keywords: rail transit traction; synchronous reluctance machine; nonlinear magnetic model; parameters identification; population evolution mechanism; synergistic optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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