This master thesis deals with the design of a model predictive controller
(MPC) for load mitigation by including LIDAR measurements
in the prediction.
For simulation purposes a nonlinear model of a wind turbine is derived.
The model is validated by its physical characteristics and by
comparing it to another model with the same level of detail and a
more complex model in the aeroelastic program LACflex. For use
in the developed MPC the nonlinear model is linearized.
For use together with the developed MPC a LIDAR model is developed.
The developed LIDAR model averages wind speed measurements
obtained from the wind field by the LIDAR model included
in LACflex.
A MPC controller that uses LIDAR measurements in the prediction
is developed and tested in MATLAB and LACflex. The MPC
is tested in MATLAB on the derived nonlinear model, against a
benchmark controller consisting of a standard PI controller with
additional damping on tower and drive train. In LACflex the MPC
is tested on a more complex turbine model and the included PI
controller in LACflex as benchmark.
Simulation results showed that by including LIDAR in MPC the
load can be mitigated and the power can be maintained