CJEE

Chinese Journal of Electrical Engineering ›› 2024, Vol. 10 ›› Issue (2): 1-15.doi: 10.23919/CJEE.2023.000046

• Regular Papers •     Next Articles

扫码分享

Review of Inductance Identification Methods Considering Inverter Nonlinearity for PMSM*

Qiwei Wang1, Jiqing Xue1, Gaolin Wang1,*, Yihua Hu2, Dianguo Xu1   

  1. 1. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150000, China;
    2. Department of Engineering, King’s College London, London WC2R 2LS, UK
  • Received:2023-07-29 Revised:2023-10-09 Accepted:2023-10-23 Online:2024-06-25 Published:2024-07-01
  • Contact: * E-mail: WGL818@hit.edu.cn
  • About author:Qiwei Wang received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from the Harbin Institute of Technology, Harbin, China, in 2015, 2017, and 2022, respectively. He is currently working as a Postdoc in Power Electronics and Electrical Drives with the School of Electrical Engineering and Automation.
    His research interests include parameter identification technique, position sensorless control and model prediction control.
    Jiqing Xue received the B.S. degrees in Electrical Engineering from the Harbin Institute of Technology, Harbin, China, in 2022. He is currently working toward the Ph.D. degree in Power Electronics and Electrical Drives at the Harbin Institute of Technology, Harbin, China.
    His current research interests include PMSM parameter identification technique and model prediction control.
    Gaolin Wang (Senior Member, IEEE) received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from the Harbin Institute of Technology (HIT), Harbin, China, in 2002, 2004, and 2008, respectively. In 2009, he joined the Department of Electrical Engineering, HIT, as a Lecturer, where he has been a Full Professor of Electrical Engineering, since 2014. He has authored more than 70 technical papers published in IEEE Transactions.
    His research interests include permanent magnet synchronous motor drives and power converters. Prof. Wang serves as a Guest Associate Editor of IEEE Transactions on Industrial Electronics, and an Associate Editor of IEEE Transactions on Transportation Electrification and IET Electric Power Applications.
    Yihua Hu (Senior Member, IEEE) received the B.S. degree in Electrical Engineering in 2003 and the Ph.D. degree in Power Electronics and Drives in 2011, both from the China University of Mining and Technology. Between 2013 and 2015, he was a Research Associate with the Power Electronics and Motor Drive Group, University of Strathclyde, UK. He is currently a Reader with the Department of Engineering, King’s College London, UK.
    His research interests include renewable generation, power electronics converters and control, electric vehicle, more electric ship/aircraft, smart energy system and non-destructive test technology. He was the recipient of Royal Society Industry Fellowship. He is the Fellow of Institution of Engineering and Technology (FIET) and a Member of UK Young Academy.
    Dianguo Xu (Fellow, IEEE) received the B.S. degree in Control Engineering from the Harbin Engineering University, Harbin, China, in 1982, and the M.S. and the Ph.D. degrees in Electrical Engineering from the Harbin Institute of Technology (HIT), Harbin, China, in 1984 and 1989, respectively.
    In 1984, he was an Assistant Professor with the Department of Electrical Engineering, HIT. Since 1994, he has been a Professor with the Department of Electrical Engineering, HIT. He was the Dean of the School of Electrical Engineering and Automation, HIT, from 2000 to 2010. He was the Vice President of the HIT, from 2014 to 2020. He has authored or coauthored more than 600 technical papers. His research interests include motor drives, PMSM servo drives, renewable energy generation technology, etc.
    Prof. Xu serves as the Co-EIC for IEEE Transactions on Power Electronics, and an Associate Editor for IEEE Transactions on Industrial Electronics and IEEE Journal of Emerging and Selected Topics in Power Electronics.
  • Supported by:
    *National Natural Science Foundation of China (52307048) and the Postdoctoral General Foundation of Heilongjiang (LBH-Z23022).

PDF

83

Abstract: Permanent magnet synchronous motors (PMSMs) are widely used in high-power-density and flexible control methods. Generally, the inductance changes significantly in real-time machine operations because of magnetic saturation and coupling effects. Therefore, the identification of inductance is crucial for PMSM control. Existing inductance identification methods are primarily based on the voltage source inverter (VSI), making inverter nonlinearity one of the main error sources in inductance identification. To improve the accuracy of inductance identification, it is necessary to compensate for the inverter nonlinearity effect. In this study, an overview of the PMSM inductance identification and the related inverter nonlinearity self-learning methods are presented.

Key words: Inductance identification, inverter nonlinearity self-learning, permanent magnet synchronous motor