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An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation

Author

Listed:
  • Zhanqing Zhou

    (School of Artificial Intelligence, Tianjin Polytechnic University, Tianjin 300387, China)

  • Xin Gu

    (School of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China)

  • Zhiqiang Wang

    (School of Artificial Intelligence, Tianjin Polytechnic University, Tianjin 300387, China)

  • Guozheng Zhang

    (School of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China)

  • Qiang Geng

    (School of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China)

Abstract

An improved direct torque control with space-vector modulation (DTC-SVM) scheme is presented in this paper. In the conventional DTC-SVM scheme, torque control performance is affected by the load conditions, due to the inappropriate linearization of the relationship between the flux angle and electromagnetic torque. Different from the conventional method, a torque controller with load angle estimation (TC-LAE) is proposed and the change rate of torque is regulated according to the variation of the load conditions, which could ensure the rapidity and consistency of torque performance at different load conditions. Meanwhile, an online permanent magnet synchronous motor and maximum torque per ampere (PMSM-MTPA) operation strategy based on the fitting solving method is proposed instead of the traditional two-dimensional look-up table, and the reference value of flux amplitude is calculated online to meet the MTPA requirement with the proposed method. The improved strategy is applied on a 6 kW PMSM, and the simulation and experimental results verified the effectiveness and the feasibility of the proposed strategy.

Suggested Citation

  • Zhanqing Zhou & Xin Gu & Zhiqiang Wang & Guozheng Zhang & Qiang Geng, 2019. "An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation," Energies, MDPI, vol. 12(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2951-:d:253568
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    References listed on IDEAS

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    1. Fengxiang Wang & Zhenbin Zhang & Xuezhu Mei & José Rodríguez & Ralph Kennel, 2018. "Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control," Energies, MDPI, vol. 11(1), pages 1-13, January.
    2. Dazhi Wang & Tianqing Yuan & Xingyu Wang & Xinghua Wang & Yongliang Ni, 2019. "Performance Improvement of Servo Control System Driven by Novel PMSM-DTC Based On Fixed Sector Division Criterion," Energies, MDPI, vol. 12(11), pages 1-16, June.
    3. Dazhi Wang & Tianqing Yuan & Xingyu Wang & Xinghua Wang & Wenhui Li, 2018. "A Composite Vectors Modulation Strategy for PMSM DTC Systems," Energies, MDPI, vol. 11(10), pages 1-15, October.
    4. Yan Xu & Tingna Shi & Yan Yan & Xin Gu, 2019. "Dual-Vector Predictive Torque Control of Permanent Magnet Synchronous Motors Based on a Candidate Vector Table," Energies, MDPI, vol. 12(1), pages 1-15, January.
    5. Qiang Song & Yiting Li & Chao Jia, 2018. "A Novel Direct Torque Control Method Based on Asymmetric Boundary Layer Sliding Mode Control for PMSM," Energies, MDPI, vol. 11(3), pages 1-15, March.
    6. Haixia Li & Jican Lin & Ziguang Lu, 2019. "Three Vectors Model Predictive Torque Control Without Weighting Factor Based on Electromagnetic Torque Feedback Compensation," Energies, MDPI, vol. 12(7), pages 1-19, April.
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    Cited by:

    1. Marcin Jastrzębski & Jacek Kabziński, 2021. "Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks," Energies, MDPI, vol. 14(18), pages 1-21, September.

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