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

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  • Linjie Ren

    (Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai 201804, China
    Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China
    National Maglev Transportation Engineering R&D Center, Tongji University, 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
    Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China
    National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China)

  • Zhiming Liao

    (National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China)

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.

Suggested Citation

  • Linjie Ren & Guobin Lin & Yuanzhe Zhao & Zhiming Liao, 2021. "Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4379-:d:536190
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    References listed on IDEAS

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    1. Nezih Gokhan Ozcelik & Ugur Emre Dogru & Murat Imeryuz & Lale T. Ergene, 2019. "Synchronous Reluctance Motor vs. Induction Motor at Low-Power Industrial Applications: Design and Comparison," Energies, MDPI, vol. 12(11), pages 1-20, June.
    2. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
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