Nonlinear predictive generalized minimum variance LPV control of wind turbines

Savvidis, Petros and Grimble, Michael and Majecki, Pawel and Pang, Yan; (2017) Nonlinear predictive generalized minimum variance LPV control of wind turbines. In: 5th IET International Conference on Renewable Power Generation (RPG) 2016. IET, GBR. ISBN 9781785613012 (https://doi.org/10.1049/cp.2016.0569)

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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.

ORCID iDs

Savvidis, Petros ORCID logoORCID: https://orcid.org/0000-0002-8872-2803, Grimble, Michael, Majecki, Pawel and Pang, Yan;
  • Item type:Book Section
    ID code:59900
    Dates:
    Date
    Event
    30 January 2017
    Published
    20 June 2016
    Accepted
    Notes:"This paper is a postprint of a paper submitted to and accepted for publication in 5th IET International Conference on Renewable Power Generation (RPG) 2016 and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library".
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:21 Feb 2017 12:02
    Last modified:11 Nov 2024 15:09
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/59900
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