Data-driven and adaptive control applications to a wind turbine benchmark model

Wind turbines are complex dynamic systems forced by stochastic wind disturbances, as well as gravitational, centrifugal, and gyroscopic loads. Since their aerodynamics are nonlinear, wind turbine modelling is thus challenging. Moreover, accurate models should contain many degrees of freedom to capture the most important dynamic effects. Therefore, the design of control algorithms for wind turbines should account for these complexities. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy. The main purpose of this study is thus to give two examples of viable and practical designs of control schemes with application to a wind turbine prototype model. Extensive simulations on the benchmark process and Monte-Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared with other different approaches, in order to evaluate advantages and drawbacks of the considered solutions. Finally, Hardware-In-the-Loop simulations serve to highlight the potential application of the proposed control strategies to real wind turbines.