Universal gravitation neural network based wind power system MPPT control method

Provided is a universal gravitation neural network based wind power system MPPT control method. According to the method, on the basis that a large amount of power-rotation speed-wind speed samples are established, a universal gravitation neural network prediction model of wind speed is established and is utilized to perform wind speed estimation, then an optimum tip speed ratio method is adopted to predict an optimal fan rotation speed corresponding to a maximum power point, the fan rotation speed is adjusted to be the predicted optimal fan rotation speed, the rotation speed is used as an initial value, and a duty ratio perturbation and observation method is adopted to set perturbation step length for tracing the maximum power of a fan. The estimation method is adopted to obtain the wind speed without a wind speed sensor, the control cost of the system can be effectively saved, and the reliability of the system can be improved. The method utilizes a universal gravitation search algorithm to optimize a neural network model, and wind speed estimation accuracy can be effectively improved. In addition, the universal gravitation neural network based wind power system MPPT control method further has the advantages of being high in tracing speed and capable of improving the powder generating efficiency of the fan.