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arXiv 2109.01646physics.flu-dynphysics.ao-ph
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基于机器学习的陆上风电机组尾流平均速度与湍流强度变异性识别:风激光雷达测量的聚类分析

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

G. Valerio Iungo, Romit Maulik, S. Ashwin Renganathan, Stefano Letizia

AI总结:

本研究针对陆上风电机组尾流特性变异性问题,结合POD降维与k-means聚类分析激光雷达测量数据,实现了不同推力系数和大气稳定度下尾流速度亏损、范围及附加湍流强度变异性的无偏表征。

摘要:

风电机组尾流是风电机组转子从入射大气风中提取动能的结果。因此,尾流内降低的平均速度和增强的湍流强度受入射风特性、风机叶片空气动力学以及风机控制设置的影响。本研究利用安装在陆上风电场的风电机组产生的孤立尾流的激光雷达(LiDAR)测量数据,表征典型运行工况下尾流平均速度和湍流强度的变异性,这些工况涵盖了广泛的大气稳定度区间、功率捕获水平,以及相应的转子推力系数。针对尾流速度场的统计分析,本研究通过k-means算法对激光雷达测量数据进行聚类,该方法能够识别最具代表性的风电机组尾流实现形式,同时避免对各类风参数和风机参数设置阈值——这类阈值可能会受到先入为主且可能错误的观念的影响。考虑到为探测轮毂高度水平面内的尾流速度场而收集的大量激光雷达样本,本研究通过将激光雷达数据投影到通过本征正交分解(POD)得到的智能截断基上,降低了实验数据集的维度。仅5个具有物理意义的POD模态的系数被认为足以复现观测到的尾流变异性,随后这些系数被输入k-means算法中,用于对激光雷达数据集进行聚类。通过对聚类后的激光雷达数据以及相关的SCADA(数据采集与监视控制)和气象数据的分析,本研究得以探究不同风机转子推力系数和大气稳定度区间下,尾流速度亏损、尾流范围以及尾流附加湍流强度的变异性。

英文摘要:

Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade aerodynamics, and the turbine control settings. In this work, LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations encompassing a breadth of atmospheric stability regimes, levels of power capture, and, in turn, rotor thrust coefficients. For the statistical analysis of the wake velocity fields, the LiDAR measurements are clustered through a k-means algorithm, which enables to identify of the most representative realizations of the wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters, which can be biased by preconceived, and potentially incorrect, notions. Considering the large number of LiDAR samples collected to probe the wake velocity field over the horizontal plane at hub height, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently-truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes, which are considered sufficient to reproduce the observed wake variability, are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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