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Smart fatigue load control on the large-scale wind turbine blades using different sensing signals
Author: Zhang Mingming | Print | Close | Text Size: A A A | 2017-11-27

This paper presented a numerical study on the smart fatigue load control of a large-scale wind turbine blade. Three typical control strategies, with sensing signals from flapwise acceleration, root moment and tip deflection of the blade, respectively, were mainly investigated on our newly developed aero-servoelastic platform. It was observed that the smart control greatly modified in-phased flow-blade interaction into an anti-phased one at primary 1P mode, significantly enhancing the damping of the fluid structure system and subsequently contributing to effectively attenuated fatigue loads on the blade, drive-chain components and tower. The aero-elastic physics behind the strategy based on the flapwise root moment, with stronger dominant load information and higher signal-to-noise ratio, was more drastic, and thus outperformed the 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.

Conclusions

To investigate the effect of the sensing signals in a smart rotor system on the fatigue load control and understand the aero-elastic physics behind, three strategies, i.e. Dx-, a- and My-strategies, corresponding to the signals from the blade flapwise tip deflection, the flapwise acceleration on the blade surface and the blade flapwise root moment, respectively, were numerically conducted on a wellknown Upwind/NREL 5 MW reference large-scale wind turbine, based on our newly developed aero-servo-elastic platform. The investigations led to three conclusions.

(1) The smart control using three sensing strategies greatly suppressed the fatigue loads on blades, drive-chain components and tower. The best performance was obtained for My-strategy case and the maximum reduction percentages in the standard deviation lay in a range of 12.0-22.5%, compared with the original collective pitch control method.

(2) For a-strategy, the effectiveness became worse near the outboard part compared to the inboard one within region II, related with the complicated flow separation near the blade tip, while the reverse was the case within region III and the general outcomes tended to be much better, due to the effective flow attachment on the blade surface caused by the turbine pitching action.

(3) The presently proposed smart control effectively turned the in-phased flow and blade vibration at the dominant 1P mode into anti-phased through the controllable DTEF excitation against the nearby fluid field. In other words, the synchronized motion between fluid and structure along the whole lade span was modified to opposition between them, leading to the obviously increased damping ratios of the fluid-structure system. Finally, the fatigue loads on the blade and thus the other turbine components were significantly impaired. This control physics was more evident for My-strategy than Dx-, and a-strategies since the signal My at the blade root was stronger and contained less interference noise, and thus reflected more primary 1P fatigue load pattern in the flapwise direction, subsequently guaranteeing the best results.

The results have been published on Renewable Energy 87 (2016) 111-119.

 

 
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