Adaptive control for variable-speed wind generation systems using advanced RBF Neural Network

Nowadays, the use of renewable energy including wind energy has increased dramatically. Thanks to the wind energy conversion systems which are more sophisticated, and the demand for new techniques and algorithms to improve the control of wind turbines; using modern or classical approaches. To optimize the efficiency of the wind turbine, the latter should vary its rotational speed in real-time to follow the instantaneous variation in wind speed. The paper proposes an intelligent controller which is based on advanced RBF-NN approach, for torque control of doubly fed induction generator based wind turbine during all value of wind speed. The ultimate objective is to maximize output power for this system. For this, the online training RBF regulator approximates the torque of the generator in according to maximum power of the wind turbine at a given wind speed. This method is analytically compared with optimal torque controller to demonstrate the relevance of this controller, illustrate maximum power point tracking and good accuracy. Uniform asymptotic stability of the tracking error origin, is analyzed via Lyapunov method.