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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, Stevenage. ISBN 9781785613012

Text (Savvidis-etal-RPG-2016-Nonlinear-predictive-generalized-minimum-variance-LPV-control-of-wind-turbines)
Savvidis_etal_RPG_2016_Nonlinear_predictive_generalized_minimum_variance_LPV_control_of_wind_turbines.pdf - Accepted Author Manuscript

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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.