Improved MPPT Control of Wind Turbines Based on Optimization of Tracking Range

The maximum power point tracking(MPPT) control based on reducing tracking range can improve the wind energy capture efficiency of wind turbines with high rotor inertia under turbulent conditions. However this method makes optimal setting of the reduction of tracking range only according to mean wind speed and other impacting factors such as turbulence intensity and several aerodynamic and structural parameters of wind turbine, for example the optimum tip speed ratio, rotational inertia and so on, are neglected. Considering that there exist complex relationships between the setting of tracking range and various factors, which are hard to be analytically described, an MPPT control method, in which the radial basis function neural network(RBFNN) is utilized to optimize the tracking range, is proposed. In the proposed improved control method the mean wind speed and turbulence intensity are taken as the input variable of neural network, and the simulated data of specific wind turbine is taken as training samples and the compensation coefficient as the output variable of neural network, thus an optimization setting of tracking range can be realized, in which both varying wind speed and the aerodynamic and structural parameters of wind turbine can be considered simultaneously. Finally, simulation calculation and comparative analysis on generated wind speed sequence are performed and the effectiveness and the advantages of the proposed control method are validated.