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Optimization of Stator Structure for Improved Accuracy in Variable Reluctance Resolvers Using Advanced Machine Learning Techniques

Author

Listed:
  • Wentao Li

    (School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road BaoShan District, Shanghai 200444, China)

  • Qiankun Liu

    (School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road BaoShan District, Shanghai 200444, China)

  • Siyang Ye

    (School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road BaoShan District, Shanghai 200444, China)

  • Surong Huang

    (School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road BaoShan District, Shanghai 200444, China)

Abstract

This study presents an optimized design for a Segmented Sinusoidal Parameter Winding with Magnetic Wedge Variable Reluctance Resolver (SSPWMW-VRR), addressing challenges like winding asymmetry and harmonic distortion in conventional designs. By integrating particle swarm optimization (PSO) for winding design, magnetic equivalent circuit (MEC) analysis for leakage flux, and machine learning techniques (XGBoost and Multi-Layer Perceptron), the stator slot shape was fine-tuned for improved accuracy. XGBoost outperformed MLP in prediction accuracy with a mean absolute error (MAE) of 0.1172. Finite element analysis (FEA) simulations and experimental validation demonstrated a reduction in position errors from ±30′ in conventional VRRs to ±5′ in the optimized design, along with significant harmonic reduction.

Suggested Citation

  • Wentao Li & Qiankun Liu & Siyang Ye & Surong Huang, 2024. "Optimization of Stator Structure for Improved Accuracy in Variable Reluctance Resolvers Using Advanced Machine Learning Techniques," Energies, MDPI, vol. 17(21), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5454-:d:1511489
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    References listed on IDEAS

    as
    1. Xinmei Wang & Yifei Wang & Tao Wu, 2022. "The Review of Electromagnetic Field Modeling Methods for Permanent-Magnet Linear Motors," Energies, MDPI, vol. 15(10), pages 1-18, May.
    2. Moritz Benninger & Marcus Liebschner & Christian Kreischer, 2023. "Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework," Energies, MDPI, vol. 16(8), pages 1-20, April.
    3. Max A. Buettner & Niklas Monzen & Christoph M. Hackl, 2022. "Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art," Energies, MDPI, vol. 15(5), pages 1-38, March.
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