Adaptive control of wind energy conversion systems using radial basis networks

Grid connected wind energy conversion systems (WECS) present interesting control demands, due to the intrinsic nonlinear characteristics of windmills and electric generators. In this paper a direct adaptive control strategy for WECS control is proposed. It is based on the combination of two control actions: a radial basis function network-based adaptive controller which drives the tracking error to zero with user specified dynamics; and a supervisory controller based on crude bounds of the system´s nonlinearities. It fires when the approximation properties of a finite neural network cannot be guaranteed. The form of the supervisory control and the adaptation law for the neural controller are derived from a Lyapunov analysis of stability. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution