A novel MPPT control design for wind-turbine generation systems using neural network compensator

This paper presents a novel maximum-power-point-tracking (MPPT) algorithm in wind-turbine generation systems using neural network compensator based on the slope of the wind-turbine mechanical power versus rotation speed to avoid the oscillation problem and effect of uncertain parameters. Because the characteristics of the wind-turbine rotation speed is determined by the wind speed and air density conditions, the technologies of changing the location of the maximum power point must be developed in the applications of MPPT control in order to make the windturbine generator get the optimal efficiency from wind energy at different operating conditions. In this study, the uncertainties in wind-turbine generation systems are compensated by a neural network, the duty cycle of dc/dc converter is determined by a PI controller, and the parameters is determined by a genetic algorithm with the help of MATLAB. From the simulation results, the validity of the proposed MPPT controller can be verified under variations of wind speed, air density, and the load electrical characteristics in wind-turbine generator systems.