风电叶片双点疲劳加载同步激振控制

Research on Synchronous Excitation Control Strategy of Wind Turbine Blade Under Double Point Fatigue Loading

针对风电叶片双点疲劳加载过程中两激振器的同步控制问题,提出了一种基于对角回归(DRNN)神经网络的交叉耦合同步控制策略。根据两激振器的同步误差与跟随误差的控制精度要求,建立交叉耦合同步控制模型;以对角回归神经网络算法设计误差补偿同步控制器,该方法将系统误差与外部干扰力矩一起输入到同步控制器,实现电机转速的自校正同步补偿,从而提高系统的鲁棒性和同步控制精度;最后,搭建一套风电叶片双点疲劳加载的试验系统进行验证。结果表明,该控制算法能较好的保证两激振器的同步状态,受外界影响较小,同步误差控制在1%以内,相对于PID同步控制器同步控制精度提高了36.67%,实现了风电叶片双点疲劳的有效加载。

Aiming at the synchronization control of two shakers in the process of two-point fatigue loading of wind turbine blades,a cross-coupling synchronization control strategy based on diagonal regression(DRNN)neural network was proposed.According to the control precision requirements of the synchronization error and following error of the two shakers,the cross-coupling synchronization control model is established.Diagonal regression neural network algorithm is used to design the error compensation synchronous controller.The system error and external disturbance torque are input to the synchronous controller,and the self-correcting synchronous compensation of motor speed is realized,so as to improve the system robustness and synchronous control accuracy.Wind-power blades finally set up a set of two point fatigue loading test system,the field test results show that the control algorithm can better ensure two excitation device of synchronous state,less affected by the outside world,the synchronization error control within 1%,compared with the PID synchronous controller synchronous control precision is increased by 36.67%,wind-power blades are realized some fatigue load effectively.