Open AccessModel predictive control of a wind turbine using short‐term wind field predictions
Author(s) -
Spencer Martin D.,
Stol Karl A.,
Unsworth Charles P.,
Cater John E.,
Norris Stuart E.
Publication year - 2013
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.1501
Subject(s) - turbine , wind power , wind speed , controller (irrigation) , model predictive control , control theory (sociology) , aeroelasticity , engineering , work (physics) , term (time) , marine engineering , environmental science , automotive engineering , computer science , aerodynamics , control (management) , meteorology , aerospace engineering , mechanical engineering , physics , electrical engineering , quantum mechanics , agronomy , artificial intelligence , biology
As wind turbines become larger and hence more flexible, the design of advanced controllers to mitigate fatigue damage and optimise power capture is becoming increasingly important. The majority of the existing literature focuses on feedback controllers that use measurements from the turbine itself and possibly an estimate or measurement of the current local wind profile. This work investigates a predictive controller that can use short‐term predictions about the approaching wind field to improve performance by compensating for measurement and actuation delays. Simulations are carried out using the FAST aeroelastic design code modelling the NREL 5 MW reference turbine, and controllers are designed for both above rated and below rated wind conditions using model predictive control. Tests are conducted in various wind conditions and with different future wind information available. It is shown that in above rated wind conditions, significant fatigue load reductions are possible compared with a controller that knows only the current wind profile. However, this is very much dependent on the speed of the pitch actuator response and the wind conditions. In below rated wind conditions, the goals of power capture and fatigue load control were considered separately. It was found that power capture could only be improved using wind predictions if the wind speed changed rapidly during the simulation and that fatigue loads were not consistently reduced when wind predictions were available, indicating that wind predictions are of limited benefit in below rated wind conditions. Copyright © 2012 John Wiley & Sons, Ltd.