Adaptive Continuous Neural Pitch Angle Control for Variable-Speed Wind Turbines

As wind energy becomes one of the fastest growing renewable energy resources, the control of large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. In this paper, an adaptive neural pitch angle control strategy is proposed for the variable-speed wind turbines (VSWT) operating in pitch control region. The control objective is to maintain the rotor speed and generator power at the prescribed reference values in the presence of external disturbance, without the need of the information of system parameters and aerodynamics. First, the order of the system dynamics is increased by defining a filtered regulation error. By this means, the non-affine characteristics of the VSWT model is transformed into a simple affine control problem and thus the feedback linearization technique can be employed. The continuousness of control signal is also guaranteed to relax the requirement on the bandwidth of actuators, and the mechanical load on pitching systems is reduced. Subsequently, an online learning approximator (OLA) is utilized to estimate the unknown nonlinear aerodynamics of the wind turbine and extend the practicability of the proposed adaptive parameter-free controller. In addition, a high-gain observer is implemented to obtain an estimation of rotor acceleration, which rejects the need of additional sensors. Rigid theoretical analysis guarantees the tracking of rotor speed/generator power and the boundedness of all other signals of the closed-loop system. Finally, the effectiveness of the proposed scheme is testified via the Wind Turbine Blockset simulation package in Matlab/Simulink environment. Moreover, comparison results reveal that the introduced solution is able to provide better regulation performance than the conventional PI counterpart.