• DocumentCode
    1233518
  • Title

    Online Parameter Estimation of Permanent-Magnet Assisted Synchronous Reluctance Motor

  • Author

    Niazi, Peyman ; Toliyat, Hamid A.

  • Author_Institution
    Seagate Technol. Co., Shrewsbury, MA
  • Volume
    43
  • Issue
    2
  • fYear
    2007
  • Firstpage
    609
  • Lastpage
    615
  • Abstract
    In comparison with the conventional synchronous reluctance machines, permanent-magnet assisted synchronous reluctance motor drives (PMa-SynRMs) have offered better torque capabilities and power factors. In order to achieve maximum efficiency, knowledge of the motor parameters is necessary to implement advanced control algorithms. Variation of motor parameters due to temperature and airgap flux has been reported in literature. Using offline models to estimate the motor parameters is known as a computationally intensive method, especially when the effect of cross saturation is included in the models. Therefore, in practical applications, online parameter estimation is favored to achieve a high-performance control system. In this paper, a simple practical method for parameter estimation of PMa-SynRM is introduced. This method is capable of identifying d- and q-axes inductances and the permanent-magnet back electromotive force
  • Keywords
    air gaps; electric machine analysis computing; parameter estimation; permanent magnet motors; power factor; reluctance motor drives; torque; airgap flux; back electromotive forces; online parameter estimation; permanent magnet assisted synchronous reluctance motor drives; power factors; torque capability; Control systems; Couplings; Magnetic flux; Parameter estimation; Permanent magnet motors; Production; Reluctance motors; Sensorless control; Synchronous motors; Torque; Online parameter estimation; SynRM; p ermanent-magnet assisted synchronous reluctance motor (PMa-SynRM); parameter variation; system identification;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/TIA.2006.890021
  • Filename
    4132882