Adaptive Model Predictive Control of DFIG-based Wind Farm: A Model-Free Control Approach

Zahra Rafiee, Rasool Heydari, Mansour Rafiee, Mohammad Reza Aghamohammadi, Jose Rodriguez, Frede Blaabjerg

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

2 Citations (Scopus)

Abstract

In this paper a new control strategy of doubly-fed induction generator based wind farms (DFIG-based WFs) is proposed. Since DFIG has an inherent nonlinear behaviour along with condition variantparameters, the system model cannot easily be extracted. Therefore, the conventional model predictive control (MPC) of DFIG-based WFs cannot perform accurately. In this paper a novel model-free adaptive MPC structure is presented to adaptively update the system model by utilizing model identification and auto-regressive moving average (AR-MAX) model, for each sampling time. Simulation results verify the performance of the proposed control structure of DFIG-based WFs compared to the conventional control strategies.

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

  • Adaptive model predictive control
  • Auto-regressive-moving-average model (ARMAX)
  • Doubly fed Induction Generator (DFIG)

ASJC Scopus subject areas

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

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