Abstract
Wind turbines are complex dynamic systems forced by stochastic wind disturbances, gravitational, centrifugal, and gyroscopic loads. Since their aerodynamics are nonlinear, wind turbine modelling is thus challenging. Therefore, the design of control algorithms for wind turbines must account for these complexities, but without being too complex and unwieldy. Therefore, the main contribution of this study consists of providing two examples of robust and viable control designs with application to a wind turbine simulator. Due to the description of the considered process, extensive simulations of this test case and Monte–Carlo analysis are the tools for assessing experimentally the achieved features of the suggested control schemes, in terms of reliability, robustness, and stability, in the presence of modelling and measurement errors. These developed control methods are finally compared with different approaches designed for the same benchmark, in order to evaluate the properties of the considered control techniques.
Highlights
Wind turbines are complex nonlinear dynamic systems forced by gravity, and stochastic wind disturbance, which are affected by gravitational, centrifugal, and gyroscopic loads
It is clear that the design of control algorithms for wind turbines has to take into account these complexities
The Variance Accounted For (VAF) value for first output was bigger than 90%, whilst bigger than 99% for the second one
Summary
Wind turbines are complex nonlinear dynamic systems forced by gravity, and stochastic wind disturbance, which are affected by gravitational, centrifugal, and gyroscopic loads. With reference to the second control method proposed in this work, the application of an on–line identification mechanism in connection with a model–based adaptive control design is considered. This control scheme belongs to the field of adaptive control. The ability of the adaptive scheme to track changes in the system parameters is exploited here in connection with the on–line computation of time–varying controller parameters, in order to maintain the required control performances The use of this identification procedure is motivated by its easy integration into the Simulink® toolbox for the design of on–line controllers [11].
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