Maximum power extraction on wind turbine systems using block-backstepping with gradient dynamics control

In this work, a novel adaptive control scheme that allows driving a stand-alone variable-speed wind turbine system to its maximum power point is presented. The scheme is based on the regulation of the optimal rotor speed point of the wind turbine. In order to compute the rotor speed reference, a model-based extremum-seeking algorithm is derived. The wind speed signal is necessary to calculate this reference, and a novel artificial neural network is derived to approximate this signal. The neural network does not need off-line learning stage, because a nonlinear dynamics for the weight vector is proposed. A block-backstepping controller is derived to stabilize and to drive the system to the optimal power point; to avoid singularities, the gradient dynamics technique is applied to this controller. Numerical simulations are carried out to show the performance of the controller and the estimator. Copyright (c) 2016 John Wiley & Sons, Ltd.