Neuroadaptive Variable Speed Control of Wind Turbine With Wind Speed Estimation

It is difficult to measure the wind speed accurately in short term. This reveals challenges for wind turbine control, especially for maximum power point tracking with adaptive control strategies. In this paper, a genetic algorithm based support vector machine model is adopted to estimate the wind speed, using physically measurable signals, such as the electrical power, pitch angle, and rotor speed, while the desired rotor speed can be obtained accordingly. Further, by combining the radial basis function neural networks with adaptive algorithms, a novel virtual parameter based neuroadaptive controller is developed to accommodate the system uncertain and external disturbances. The effectiveness and performances of the proposed method are validated and demonstrated with FAST (Fatigue, Aerodynamics, Structures, and Turbulence) and Simulink.