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Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)

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  • Mohamed El-Nemr

    (Electromagnetic Energy Conversion Laboratory, Tanta University, Tanta 31527, Egypt
    Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta 31527, Egypt)

  • Mohamed Afifi

    (Electromagnetic Energy Conversion Laboratory, Tanta University, Tanta 31527, Egypt)

  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Aldawaser 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Mohamed Ibrahim

    (Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9000 Ghent, Belgium
    FlandersMake@UGent—Corelab EEDT-MP, 3001 Leuven, Belgium
    Electrical Engineering Department, Kafrelshiekh University, Kafrelshiekh 33511, Egypt)

Abstract

The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM.

Suggested Citation

  • Mohamed El-Nemr & Mohamed Afifi & Hegazy Rezk & Mohamed Ibrahim, 2021. "Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)," Mathematics, MDPI, vol. 9(5), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:576-:d:512835
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    References listed on IDEAS

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    1. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
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    Cited by:

    1. Chiweta Emmanuel Abunike & Ogbonnaya Inya Okoro & Sumeet S. Aphale, 2022. "Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance," Energies, MDPI, vol. 15(16), pages 1-23, August.
    2. Weihua Qian & Hang Xu & Houjin Chen & Lvqing Yang & Yuanguo Lin & Rui Xu & Mulan Yang & Minghong Liao, 2024. "A Synergistic MOEA Algorithm with GANs for Complex Data Analysis," Mathematics, MDPI, vol. 12(2), pages 1-30, January.
    3. Yan Li & Yifan Liu & Shasha Li & Leijie Qi & Jun Xie & Qing Xie, 2022. "A Novel Multi-Objective Optimal Design Method for Dry Iron Core Reactor by Incorporating NSGA-II, TOPSIS and Entropy Weight Method," Energies, MDPI, vol. 15(19), pages 1-15, October.
    4. Mingyu Choi & Gilsu Choi & Gerd Bramerdorfer & Edmund Marth, 2022. "Systematic Development of a Multi-Objective Design Optimization Process Based on a Surrogate-Assisted Evolutionary Algorithm for Electric Machine Applications," Energies, MDPI, vol. 16(1), pages 1-19, December.

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