• DocumentCode
    3603540
  • Title

    Grid Parameter Estimation Using Model Predictive Direct Power Control

  • Author

    Arif, Bilal ; Tarisciotti, Luca ; Zanchetta, Pericle ; Clare, Jon C. ; Degano, Marco

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Univ. of Nottingham, Nottingham, UK
  • Volume
    51
  • Issue
    6
  • fYear
    2015
  • Firstpage
    4614
  • Lastpage
    4622
  • Abstract
    This paper presents a novel finite-control-set model predictive control (FS-MPC) approach for grid-connected converters. The control performance of such converters may get largely affected by variations in the supply impedance, especially for systems with low short-circuit ratio values. A novel idea for estimating the supply impedance variation, and hence the grid voltage, using an algorithm embedded in the MPC is presented in this paper. The estimation approach is based on the difference in grid voltage magnitudes at two consecutive sampling instants, calculated on the basis of supply currents and converter voltages directly within the MPC algorithm, achieving a fast estimation and integration between the controller and the impedance estimator. The proposed method has been verified, using simulation and experiments, on a three-phase two-level converter.
  • Keywords
    power control; power convertors; predictive control; voltage control; FS-MPC approach; consecutive sampling instants; converter voltages; finite-control-set model predictive control approach; grid parameter estimation; grid voltage magnitudes; grid-connected converters; impedance estimator; model predictive direct power control; short-circuit ratio values; supply currents; supply impedance variation; three-phase two-level converter; Estimation; Impedance; Inductance; Reactive power; Resistance; Switches; Voltage control; AC???DC power conversion; grid impedance estimation; model predictive control (MPC); power conversion; power system dynamic stability;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/TIA.2015.2453132
  • Filename
    7151796