Multiple-vector model predictive power control for grid-tied wind turbine system with enhanced steady-state control performance

Zhenbin Zhang, Hui Fang, Feng Gao, Jose Rodriguez, Ralph Kennel

Research output: Contribution to journalArticlepeer-review

198 Scopus citations

Abstract

Direct model predictive control (DMPC) is a promising alternative for power electronics and electric drives. It takes the switching nonlinearity of the power converters and system constraints into consideration, without using an extra modulator. However, its one-switching-vector-per-control-interval character leads to big ripples of the control variables. Therefore, with a similar sampling frequency, its steady-state performance is not satisfying. In this paper, we propose a multiple-vector direct model predictive power control (MV-DMPPC) concept for the grid-side power converter control of a back-to-back converter permanent-magnet synchronous generator wind turbine system, using a fully field programmable gate array based solution. The proposed control scheme is compared with the classical DMPPC and two recently reported DMPPC scheme with duty cycle optimizations. Both simulation and experimental tests validate that the control performances are evidently improved with the proposed MV-DMPPC solution.

Original languageEnglish
Article number7878550
Pages (from-to)6287-6298
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Volume64
Issue number8
DOIs
StatePublished - Aug 2017

Keywords

  • Direct model predictive control (DMPC)
  • Field programmable gate array (FPGA) digital control
  • Grid-tied active front end (AFE)
  • Performance-enhanced direct power control (DPC)
  • Time-optimal control
  • Wind turbine systems with permanent-magnet synchronous generator (PMSG)

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