基于弹性自适应人工鱼群-BP神经网络的风轮节距控制环

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

为了研制一种调节桨叶节距角的智能控制器,使风力发电机组在变化的风力中获得最大的能量并使转速、功率和机械负载变化最小,提出了一种基于弹性自适应人工鱼群-BP神经网络的风轮节距控制环并用于风轮节距角控制,分析了弹性自适应人工鱼群优化算法-BP神经网络,建立智能控制的风力发电机组模型。使用该方法模拟了在不同桨叶节距角下功率系数、叶尖速比、功率和电压变化,模拟值与实测值进行了对比。试验表明,模拟值与实测值比较接近,仿真效果较佳。结果表明该方法原理正确,符合实际调节及微机控制,可用于实时控制。

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.