An Indirect Adaptive Control Paradigm for Wind Generation Systems

Globally, there has been a significant evolution in the development of wind energy. Nevertheless, the major difference between the highly stochastic nature of wind speed and the desired demands regarding the electrical energy quality and system stability is the main concern in wind energy system. Hence, wind energy generation according to the standard parameters imposed by the power industry is unachievable without the essential involvement of advanced control technique. In this book chapter, a novel indirect adaptive control for wind energy systems is proposed considering real load demand and weather parameters. The performance of existing neuro-fuzzy scheme is improved further using a Hermite wavelet in the proposed architecture. The parameters of the controller are trained adaptively online via backpropagation algorithm. The proposed control law adopts the free direct control model which shorten the weight of the lengthy pre-learning, and memory requirements for real time application. Various computer simulation results and performance comparison indexes are given to show that the proposed control law is better in terms of efficiency, output power and steady-state performance over the existing state-of-the-art.