Abstract
Wake meandering disturbs the stability of the far wake field and thus increases the fatigue loads of downstream wind turbines. A deep understanding of this phenomenon under atmospheric boundary layers and its relation to the structural loads helps to better model the dynamic wake and alleviate adverse effects. A large eddy simulation and an actuator line model are introduced in the present work to simulate the wake field and aerodynamic loads of wind turbines with different longitudinal spacings. By temporal filtering and the gaussian fitting method, the wake center and edge are precisely defined, and the dynamic wake characteristics, including the wake width, oscillation amplitude, and frequency, are described based on the statistical data of the simulated flow field. Results reveal that the wake meandering is caused by both large-scale atmospheric structure and the unstable vortex shed from the rotor because two distinct meandering frequency ranges are detected. As the atmosphere instability increases, the former becomes the dominant inducing factor of the meandering movements. Further, the analysis of the correlation between the inflow characteristics and the wake deflection shows that the Taylor hypothesis remains valid within a distance of over a thousand meters under both neutral and convective boundary layers, proving the feasibility of using this hypothesis for wake evolution prediction. In addition, our study shows that the fluctuation of blade root moment and yaw moment is significantly intensified by the meandering wake, with their standard deviation is augmenting by over two times under both atmospheric conditions. The power spectrum illustrates that the component with rotor rotation frequency of the former is sensible to the wake effect, but for the latter, the power spectrum density of all frequencies is increased under the meandering wake. These indicate that the fatigue loads will be underestimated without considering the wake meandering effect. Moreover, the high correlation between the wake deflection and yaw moment implies that we can predict yaw moment based on the incoming flow information with high accuracy.
Highlights
The wind energy industry has seen rapid progress in the last decade, contributing a global cumulative power output of 591GW [1]
A horizontal axis wind turbine (HAWT) is the most common wind energy converter in commercial wind farms, and every working HAWT will inevitably leave a tube-shaped wake region characterized by relative low wind speed and high turbulence intensity, which extends over a 10-diameter distance and reduces the performance of turbines located downstream [2,3]
We explained the mechanism of wake meandering by spectrum and correlation analysis and investigated the frequency and amplitude of wake movements in lateral and vertical directions
Summary
The wind energy industry has seen rapid progress in the last decade, contributing a global cumulative power output of 591GW [1]. The intensification of climate change and the development of related technologies will lead to a continuous prosperity of wind power in the foreseeable future. Some analytical wake models [4,5,6] have been proposed to describe the velocity distribution behind wind turbines, aiming to predict and optimize the output of wind farms. Widely applied to engineering practice over past decade, these models only provide static wake field results at a large time scale and cannot support a real-time cooperated wind farm control strategy. Further research is required to gain a deep understanding of the dynamic characteristics of wind turbine wake
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