MIMO Predictive Control of a Wind Turbine

nonlinear wind-turbine model is studied. The wind turbine process is represented by a set of local linear models which are obtained by piecewise linearization of the nonlinear mathematical model at different wind speeds. In order to provide zero steady-state offset in case of a disturbance or model/plant mismatch the model is augmented with disturbance model. Estimates of the states and wind are obtained with Extended Kalman Filter (EKF). The estimated wind is used for computation of weights of corresponding local models. Linear model parameters and estimated states are then used within predictive control strategy for computation of control signals. Due to different control demands in different operating regimes of the wind turbine the weighting matrices are also scheduled for different wind speeds. Simulations on the 5MW wind-turbine model in turbulent wind and comparison with baseline PI controller show that the wind-turbine system can be successfully controlled at different operating regions by this methodology.

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