The present article proposes a framework for validation of stationary wake models that wind developers can use to predict the energy production of a wind power plant more accurately. The application of this framework provides a new way to quantify the uncertainty of annual energy production predictions. Additionally this methodology enables the fair comparison of different wake models. Furthermore the methodology enables the estimation of how much information can be obtain from a measurement dataset to quantify model inadequacy. In the present work the proposed framework is applied to the Horns Rev 1 offshore wind power plant. The model uncertainty of a modified N. O. Jensen wake model under uncertain undisturbed flow conditions was studied. Evidence of model inadequacy is found in terms of a bias in the predicted AEP distribution. It was found that the use of the official power curve compensates the errors in the wake model, as a consequence a larger uncertainty of the overall model is predicted. Furthermore a study of wake model benchmarking based on filtered flow cases indicates that measurement uncertainty in the wind speed and wind direction is large enough to obtain any evidence of model inaccuracy even for the simplest wake models.
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机译:本文提出了一种用于验证固定尾流模型的框架,风力开发商可以使用该框架来更准确地预测风力发电厂的能源生产。该框架的应用提供了一种量化年度能源产量预测不确定性的新方法。另外,这种方法可以公平比较不同的唤醒模型。此外,该方法能够估计可从测量数据集中获得多少信息以量化模型不足。在目前的工作中,拟议的框架被应用于Horns Rev 1海上风力发电厂。研究了在不确定的无扰动流动条件下,改进的N. O. Jensen尾流模型的模型不确定性。根据预测的AEP分布中的偏差发现了模型不足的证据。已经发现,使用官方功率曲线可以补偿尾流模型中的误差,因此,可以预测整个模型的不确定性。此外,基于滤波后的流量情况进行的尾流模型基准测试研究表明,即使对于最简单的尾流模型,风速和风向的测量不确定性也足以获得任何模型误差的证据。
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