不同传感信号对风电叶片载荷智能控制影响研究

Effect of Various Sensing Signals on Smart Load Control of Wind Turbine Blade

本文在自主搭建的DTEF（柔性尾缘襟翼）＂智能叶片＂仿真实验平台的基础上,讨论了三种不同传感信号方案,即信号来自叶片挥舞方向加速度,叶尖位移和叶根挥舞力矩,对控制效果影响。首先,本文分析了四组不同位置处加速度信号对控制效果的影响,接着对比了三种传感器信号方案的优劣,最后详细分析了相关流动控制机理。通过详细的讨论,本文得出结论：1）由于复杂流动分离和变桨作用,导致风力机叶片在Ⅱ区运行时选取加速度传感信号越靠近叶尖,控制效果越差,与Ⅲ区运行时相反;2）三种传感器信号方案中,选取叶根挥舞力矩为传感信号时控制效果最佳,相比原有主机控制最大降低载荷12.0%～22.5%;3）DTEF的作用扰乱了控制前叶片流动-结构间较好的同步性,削弱了叶片系统间的气弹耦合作用,从而有效地减少了叶轮受载。

This paper presented a numerical study on the smart fatigue load control of a large-scale wind turbine blade.Three control strategies,with representative sensing signals from flapwise acceleration,root moment and tip deflection of the blade,respectively,were mainly investigated on our newly developed aero-servo-elastic platform.It was observed that the smart control greatly modified in-phased flow-blade interaction into anti-phased one at primary IP mode,significantly enhancing the damping of fluid-structure system and subsequently contributing to effectively attenuated fatigue loads on blade,drive-chain components and tower.The aero-elastic physics behind the strategy base on the feedback signal from the flapwise root moment,with stronger dominant load information and higher signal-to-noise ratio,was more drastic,and thus outperformed other two strategies,leading to the maximum reduction percentages of the fatigue load within a range of 12.0%～22.5%,in contrast to the collective pitch control method.The finding pointed to a crucial role the sensing signal played in the smart blade control.In addition,the performances within region Ⅲ were much better than those within region Ⅱ,exhibiting the benefit of the smart rotor control since most of the fatigue damage was believed to be accumulated beyond the rated wind speed.