Simple and Robust Finite-Control-Set Model Predictive Control for DFIGs in Wind Turbine Systems

Mohamed Abdelrahem, Ralph Kennel, Christoph M. Hackl, Jose Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

This paper proposed a simple and robust Finite-control-set model-predictive control (FCS-MPC) technique for doubly-fed induction generators (DFIGs) in wind turbine applications. In the suggested control technique, the machine model and demanded reference currents are exploited to give the reference voltage vector (RVV). Furthermore, the disturbance caused by parameters variations or by any un-modeled dynamics is estimated by a simple observer and added to the RVV calculations. Finally, based on the location of this RVV, two iterations are required for the quality function to produce the optimal switching state to apply. The suggested FCS-MPC scheme was experimentally validated and the response was contrasted with the classical FCS-MPC.

Original languageEnglish
Title of host publication2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728158495
DOIs
Publication statusPublished - Feb 2020
Event11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020 - Tehran, Iran, Islamic Republic of
Duration: 4 Feb 20206 Feb 2020

Publication series

Name2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020

Conference

Conference11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
Country/TerritoryIran, Islamic Republic of
CityTehran
Period4/02/206/02/20

Keywords

  • DFIG
  • disturbance observer
  • Predictive control
  • wind turbine

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization

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