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Multi-Objective Optimization of the Halbach Array Permanent Magnet Spherical Motor Based on Support Vector Machine

Author

Listed:
  • Hongfeng Li

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300000, China)

  • Lifeng Cui

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300000, China)

  • Zigang Ma

    (Cable Branch of State Grid Tianjin Electric Power Company, Tianjin 300000, China)

  • Bin Li

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300000, China)

Abstract

The fundamental harmonic amplitude and waveform distortion rate of the air-gap flux density directly affect the performance of a permanent magnet spherical motor (PMSM). Therefore, in the paper, the axial air-gap magnetic field including the end leakage of the Halbach array PMSM is analyzed and optimized. In order to reduce the calculation time of the objective function, the air gap magnetic field model adopts a non-linear regression model based on support vector machine (SVM). At the same time, the improved grid search (GS) algorithm is used to optimize the parameters of SVM model, which improves the efficiency and accuracy of parameter optimization. Considering the influence of moment of inertia on the dynamic response of the motor, the moment of inertia of the PMSM is calculated. This paper takes the air gap magnetic density fundamental wave amplitude, waveform distortion rate and rotor moment of inertia as the optimization objectives. The particle swarm optimization (PSO) algorithm is used to optimize the motor structure with multiple objectives. The optimal structure design of the PMSM is selected from all of non-dominated solutions by the technique for order preference by similarity to an ideal solution (TOPSIS). The performance of the motor before and after the optimization is analyzed by the method of finite element (FEM) and experimental verification. The results verify the effectiveness and efficiency of the optimization method for the optimal structure designing of the complex PMSM.

Suggested Citation

  • Hongfeng Li & Lifeng Cui & Zigang Ma & Bin Li, 2020. "Multi-Objective Optimization of the Halbach Array Permanent Magnet Spherical Motor Based on Support Vector Machine," Energies, MDPI, vol. 13(21), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5704-:d:438141
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    References listed on IDEAS

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    1. Hisahide Nakamura & Yukio Mizuno, 2020. "Method for Diagnosing a Short-Circuit Fault in the Stator Winding of a Motor Based on Parameter Identification of Features and a Support Vector Machine," Energies, MDPI, vol. 13(9), pages 1-15, May.
    2. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    3. Dongxiao Niu & Shuyu Dai, 2017. "A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Gre," Energies, MDPI, vol. 10(3), pages 1-20, March.
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    Cited by:

    1. Changchuang Huang & Baoquan Kou & Xiaokun Zhao & Xu Niu & Lu Zhang, 2022. "Multi-Objective Optimization Design of a Stator Coreless Multidisc Axial Flux Permanent Magnet Motor," Energies, MDPI, vol. 15(13), pages 1-13, June.
    2. Gongrun Wang & Yongxing Wang & Lifan Zhang & Shutian Xue & Enyuan Dong & Jiyan Zou, 2021. "A Novel Model of Electromechanical Contactors for Predicting Dynamic Characteristics," Energies, MDPI, vol. 14(22), pages 1-15, November.

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