Nonlinear predictive generalized minimum variance LPV control of wind turbines

Petros Savvidis, Michael Grimble, Pawel Majecki, Yan Pang

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)
75 Downloads (Pure)

Abstract

More advanced control strategies are needed for use with wind turbines, due to increases in size and performance requirements. This applies to both individual wind turbine controls and for the total coordinated controls for wind farms. The most successful advanced control method used in other industries is predictive control, which has the unique ability to handle hard constraints that limit system performance. However, wind turbine control systems are particularly difficult in being very nonlinear and dependent upon the external parameter variations which determine behaviour. Nonlinear controllers are often complicated to implement. The approach proposed here is to use one of the latest predictive control methods which can be used with linear parameter varying (LPV) models. These can approximate the behaviour of nonlinear wind turbines and provide a simpler control structure to implement. The work has demonstrated the feasibility and benefits that may be obtained.
Original languageEnglish
Title of host publication5th IET International Conference on Renewable Power Generation (RPG) 2016
Place of PublicationStevenage
Number of pages6
ISBN (Electronic)9781785613012
DOIs
Publication statusPublished - 30 Jan 2017
Event 5th IET International Conference on Renewable Power Generation (RPG) 2016 - IET London: Savoy Place, London, United Kingdom
Duration: 21 Sep 201623 Sep 2016
http://conferences.theiet.org/rpg-europe/about/index.cfm

Conference

Conference 5th IET International Conference on Renewable Power Generation (RPG) 2016
CountryUnited Kingdom
CityLondon
Period21/09/1623/09/16
Internet address

Keywords

  • wind turbine control
  • modelling
  • LPV
  • predictive control

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