利用现场尾流转向实验的激光雷达测量验证可解释的数据驱动尾流模型

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Wind Energy Science Pub Date : 2023-05-11 DOI:10.5194/wes-8-747-2023
B.A.M. Sengers, G. Steinfeld, P. Hulsman, M. Kühn
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引用次数: 2

摘要

摘要数据驱动的尾流模型最近在从数值数据集再现尾流特征方面显示出很高的精度。本研究使用配备激光雷达的商用风力涡轮机的尾流测量和附近气象桅杆的入流测量来验证可解释数据驱动的替代尾流模型。经过训练的数据驱动模型然后与最先进的分析尾流模型进行比较。一种多平面激光雷达测量策略捕获了在偏航失调期间尾流旋度的发生,这在现场尚未得到确切的观察。两种尾迹模型的比较表明,数据驱动模型对位于下游4个转子直径处的虚拟涡轮可用功率的估计明显优于分析模型。根据所使用的输入变量,平均绝对百分比误差减少了19%至36%。特别是在涡轮偏航失调和高垂直剪切情况下,数据驱动模型的性能更好。进一步分析表明,当仅使用监控和数据采集(SCADA)数据作为输入时,数据驱动模型的准确性几乎不受影响。虽然结果仅针对单一涡轮类型,下游距离和偏航失调范围,但本研究的结果被认为证明了数据驱动尾流模型的潜力。
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Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
Abstract. Data-driven wake models have recently shown a high accuracy in reproducing wake characteristics from numerical data sets. This study used wake measurements from a lidar-equipped commercial wind turbine and inflow measurements from a nearby meteorological mast to validate an interpretable data-driven surrogate wake model. The trained data-driven model was then compared to a state-of-the-art analytical wake model. A multi-plane lidar measurement strategy captured the occurrence of the wake curl during yaw misalignment, which had not yet conclusively been observed in the field. The comparison between the wake models showed that the available power estimations of a virtual turbine situated four rotor diameters downstream were significantly more accurate with the data-driven model than with the analytical model. The mean absolute percentage error was reduced by 19 % to 36 %, depending on the input variables used. Especially under turbine yaw misalignment and high vertical shear, the data-driven model performed better. Further analysis suggested that the accuracy of the data-driven model is hardly affected when using only supervisory control and data acquisition (SCADA) data as input. Although the results are only obtained for a single turbine type, downstream distance and range of yaw misalignments, the outcome of this study is believed to demonstrate the potential of data-driven wake models.
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
期刊最新文献
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