Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines

唤醒 海洋工程 海上风力发电 空气动力学 偏移量(计算机科学) 风力发电 风洞 结构工程 工程类 环境科学 航空航天工程 计算机科学 电气工程 程序设计语言
作者
Jili Sun,Zheng Chen,Hao Yu,Shan Gao,Bin Wang,You Ying,Yong Sun,Peng Qian,Dahai Zhang,Yulin Si
出处
期刊:Renewable Energy [Elsevier BV]
卷期号:199: 71-86 被引量:14
标识
DOI:10.1016/j.renene.2022.08.137
摘要

Wind turbines in an offshore wind farm are usually allocated in a restricted area, which will result in wake interactions for downstream turbines. In order to mitigate wake effects on power and loads, wake redirection control (WRC) has been proposed to steer wake away from downstream turbines by intentionally yawing the upstream ones. However, wake interactions in combination with yaw-misalignment would produce complex structural loading behaviours, which should be carefully evaluated in control design. In this respect, priori knowledge on fatigue load contribution from wake and yaw-offset could be beneficial for multi-objective control optimisation and its real-time implementation. Therefore, we aim to quantitatively evaluate both yaw-offset and wake induced fatigue loads on offshore wind turbines in this work. More specifically, a numerical turbulent wind-field generator is established by including the wake deficit modelling feature, so that aero-elastic simulation with parametrically controlled wake inflow becomes possible. Then, aero-elastic simulations covering a wide range of waked inflow and yaw-offset conditions are performed, establishing a comprehensive database for tower and blade damage equivalent loads, where different load trends can be observed. In addition, a load assessment model is established by polynomial regression, and correlation analysis results indicate the dominant factors on fatigue loads are wind velocity and turbulence intensity. Besides, yaw-offset is more important than wakes for rated conditions, and vice versa for below and above-rated conditions. Moreover, wind tunnel results have also shown generally consistent trends with model predictions. The established load database and regression model could be used as the load indicator for future design optimisation of wind farm WRC.
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