Nonlinear model predictive control of wind turbines using LIDAR

LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts in wind energy such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller with a baseline controller, showing the advantages of using the wind predictions in the optimization problem to reduce wind turbine extreme and fatigue loads on tower and blades as well as to limit the pitch rates. The wind information is obtained by a detailed simulation of a LIDAR system. The controller design is evaluated and tested in a simulation environment with coherent gusts and a set of turbulent wind fields using a detailed aeroelastic model of the wind turbine over the full operation region. Results show promising load reduction up to 50% for extreme gusts and 30% for lifetime fatigue loads without negative impact on overall energy production. This controller can be considered as an upper bound for other LIDAR assisted controllers that are more suited for real time applications. Copyright © 2012 John Wiley & Sons, Ltd.

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