IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i8p2256-d538015.html
   My bibliography  Save this article

A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor

Author

Listed:
  • 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)

  • 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)

  • Zhiming Liao

    (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)

Abstract

Due to the particularity of the synchronous reluctance motor (SynRM) structure, a novel high-performance model predictive torque control (MPTC) method was proposed to reduce the high torque ripple and improve the performance and efficiency of the motor. First, the precise parameters of the SynRM reflecting the magnetic saturation characteristics were calculated using finite element analysis (FEA) data, and the torque and flux linkage maximum torque per ampere (MTPA) trajectory was derived by considering the saturation characteristics. Then, an MPTC model of a SynRM with duty cycle control was established, the MTPA trajectory stored in a look-up table was introduced into the control model, and the duration of the active voltage vector in one control cycle was calculated by evaluating the torque error. Finally, an experimental platform based on a SynRM prototype was built, and various performance comparison experiments were carried out for the proposed MPTC method. The experimental results show that the proposed method could reduce the torque ripple of the motor, the performance of the motor was significantly improved under various working conditions, and its correctness and effectiveness were verified.

Suggested Citation

  • Yuanzhe Zhao & Linjie Ren & Zhiming Liao & Guobin Lin, 2021. "A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor," Energies, MDPI, vol. 14(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2256-:d:538015
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/8/2256/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/8/2256/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Ahmed Farhan & Mohamed Abdelrahem & Amr Saleh & Adel Shaltout & Ralph Kennel, 2020. "Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation," Energies, MDPI, vol. 13(2), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rajesh Poola & Tsuyoshi Hanamoto, 2022. "Automated QFT-Based PI Tuning for Speed Control of SynRM Drive with Analytical Selection of QFT Control Specifications," Energies, MDPI, vol. 15(2), pages 1-17, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paweł Idziak & Krzysztof Kowalski, 2021. "Analysis of Selected Operating States of the Line Start Synchronous Reluctance Motor Using the Finite Element Method," Energies, MDPI, vol. 14(20), pages 1-18, October.
    2. Giovanni Bucci & Fabrizio Ciancetta & Edoardo Fiorucci & Simone Mari & Maria Anna Segreto, 2019. "The Measurement of Additional Losses in Induction Motors: Discussion about the Actually Achievable Uncertainty," Energies, MDPI, vol. 13(1), pages 1-13, December.
    3. Chiweta E. Abunike & Udochukwu B. Akuru & Ogbonnaya I. Okoro & Chukwuemeka C. Awah, 2023. "Sizing, Modeling, and Performance Comparison of Squirrel-Cage Induction and Wound-Field Flux Switching Motors," Mathematics, MDPI, vol. 11(16), pages 1-24, August.
    4. Hyunwoo Kim & Yeji Park & Seung-Taek Oh & Hyungkwan Jang & Sung-Hong Won & Yon-Do Chun & Ju Lee, 2020. "A Study on the Rotor Design of Line Start Synchronous Reluctance Motor for IE4 Efficiency and Improving Power Factor," Energies, MDPI, vol. 13(21), pages 1-15, November.
    5. Duc-Kien Ngo & Min-Fu Hsieh, 2019. "Performance Analysis of Synchronous Reluctance Motor with Limited Amount of Permanent Magnet," Energies, MDPI, vol. 12(18), pages 1-20, September.
    6. Vadim Kazakbaev & Aleksey Paramonov & Vladimir Dmitrievskii & Vladimir Prakht & Victor Goman, 2022. "Indirect Efficiency Measurement Method for Line-Start Permanent Magnet Synchronous Motors," Mathematics, MDPI, vol. 10(7), pages 1-14, March.
    7. Rajesh Poola & Tsuyoshi Hanamoto, 2022. "Automated QFT-Based PI Tuning for Speed Control of SynRM Drive with Analytical Selection of QFT Control Specifications," Energies, MDPI, vol. 15(2), pages 1-17, January.
    8. Hyunwoo Kim & Yeji Park & Huai-Cong Liu & Pil-Wan Han & Ju Lee, 2020. "Study on Line-Start Permanent Magnet Assistance Synchronous Reluctance Motor for Improving Efficiency and Power Factor," Energies, MDPI, vol. 13(2), pages 1-15, January.
    9. Victor Goman & Vladimir Prakht & Vadim Kazakbaev & Vladimir Dmitrievskii, 2021. "Comparative Study of Energy Consumption and CO 2 Emissions of Variable-Speed Electric Drives with Induction and Synchronous Reluctance Motors in Pump Units," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    10. 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.
    11. Mohamed Abdelrahem & José Rodríguez & Ralph Kennel, 2020. "Improved Direct Model Predictive Control for Grid-Connected Power Converters," Energies, MDPI, vol. 13(10), pages 1-14, May.
    12. Florin Pop-Pîgleşan & Adrian-Cornel Pop & Claudia Marțiş, 2021. "Synchronous Reluctance Machines for Automotive Cooling Fan Systems: Numerical and Experimental Study of Different Slot-Pole Combinations and Winding Types," Energies, MDPI, vol. 14(2), pages 1-28, January.
    13. Ibrahim Harbi & Mohamed Abdelrahem & Mostafa Ahmed & Ralph Kennel, 2020. "Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter," Sustainability, MDPI, vol. 12(19), pages 1-23, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2256-:d:538015. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.