LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes
LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes
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DOI：
10.1002/we.2430
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发表时间：
2019-06
期刊：
影响因子：
4.1
通讯作者：
L. Zhan;S. Letizia;G. Valerio Iungo
中科院分区：
文献类型：
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作者：
L. Zhan;S. Letizia;G. Valerio Iungo
使用 UTD 移动激光雷达站对位于德克萨斯州的一个陆上风电场进行了风测量，目的是表征不同轮毂高度风速和静态大气稳定状态下风力涡轮机尾流的演变。风速场通过扫描多普勒测风激光雷达测量，而大气边界层和涡轮参数分别通过气象塔和SCADA监测。尾流测量结果被聚类，它们的集合统计数据作为轮毂高度风速和大气稳定状态的函数进行检索，其特征是用散装理查森数或轮毂高度处的风湍流强度来表征。激光雷达测量结果的聚类分析表明，涡轮机推力系数是驱动近尾流速度差变化的主要参数。相比之下，大气稳定性对近尾流速度场的影响可以忽略不计，而对远尾流演化和恢复的影响则显着。观察到尾流恢复率的次要影响是转子推力系数的函数。对于更高的推力系数，增强的尾流产生的湍流促进尾流恢复。使用转子推力系数和轮毂高度处的传入湍流强度作为输入，制定半经验模型来预测最大尾流速度赤字作为下游距离的函数。 LiDAR 测量的聚类分析和通过 Barnes 方案计算的集合统计数据能够生成用于开发和评估风电场模型的有价值的数据集。
Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind‐turbine wakes for different hub‐height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met‐tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub‐height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near‐wake velocity field, while it affects noticeably the far‐wake evolution and recovery. A secondary effect on wake‐recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake‐generated turbulence fosters wake recovery. A semi‐empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models.