Clinopodium pitch control loop based on adaptive artificial fish school algorithm-BP neural networks

For developing an intelligent controller for regulating blade pitch angle and wind turbine to reach the control objectives, which get maximum energy and achieve the smallest changes of rotational speed, power and mechanical load in change of wind, a technique of clinopodium pitch control loop based on resilient adaptive artificial fish school algorithm-backpropagation neural network was proposed to control clinopodium pitch angle, resilient adaptive artificial fish school optimization algorithm–backpropagation neural network was analyzed, and wind turbine model of intelligent control was established. Changes of power coefficients, tip speed ratio, power and voltage were simulated under different pitch angles of blades by the method, and the simulated values were compared with the measured values. Experimental results indicated that the simulated values were very closed to the measured values, and the simulated values were better. This result shows that the principle of the method is correct, the wind turbine model of intelligent control is accordant to actual regulation and convenient for microcomputer control, controller can be used for real-time control.