Predictive Torque Control without Weighting Factors for Doubly-Fed Induction Generators in Wind Turbine Applications

Mohamed Abdelrahem, Ralph Kennel, Christoph Hackl, Jose Rodriguez

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

Abstract

This paper presents a predictive torque control (PTC) method for doubly-fed induction generators (DFIGs) without weighting factors. The proposed control technique is based on calculating the d-axis reference current from the reference torque. Furthermore, the q-axis reference current is employed to regulate the reactive power exchange between the stator of the DFIG and the grid. Then, thereference voltage vector (VV) is directly computed from the reference current vector using the deadbeat principle. Finally, according to the location of this reference VV, only three evaluations of the cost function are required. The cost function includes only the error between the reference VV and the candidates ones, which eliminates the need for weighting factors. Experimental results using a dSPACE DS1007 real-time platform and a 10kW DFIG are presented to verify the feasibility of theproposed control method.

Original languageEnglish
Title of host publication2020 IEEE 21st Workshop on Control and Modeling for Power Electronics, COMPEL 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728171609
DOIs
Publication statusPublished - 9 Nov 2020
Event21st IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2020 - Aalborg, Denmark
Duration: 9 Nov 202012 Nov 2020

Publication series

Name2020 IEEE 21st Workshop on Control and Modeling for Power Electronics, COMPEL 2020

Conference

Conference21st IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2020
Country/TerritoryDenmark
CityAalborg
Period9/11/2012/11/20

Keywords

  • doubly-fed induction generator
  • Predictive torque control
  • weighting factors
  • wind turbine

ASJC Scopus subject areas

  • Control and Optimization
  • Modelling and Simulation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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