Predicting offshore wind turbine wake and output power is crucial for optimizing wind farm layouts and maximizing wind energy production. In recent years, several Computational Fluid Dynamics methods have been developed to predict wind turbine wake and output power and demonstrated good performance compared with traditional analytical models. However, Computational Fluid Dynamics often involve high computational costs in offshore wind farm design because a wide range of offshore wind conditions need to be considered for turbines with different inter-turbine spacings. To ensure both the fidelity and efficiency for predicting offshore wind turbine wake and output power, Large Eddy Simulation and Convolutional Neural Network are utilized in this study. The Large Eddy Simulation effectively integrates the Actuator Line Method and Discretizing and Synthesizing Random Flow Generation to generate wake velocity, wake turbulence intensity, and output power for a stand-alone turbine under different incoming wind speeds and turbulence intensities. Using the generated dataset, Convolutional Neural Network effectively captures the relationship between inputs and outputs for the stand-alone turbine. The predicted wake data for the turbine can then act as input to estimate the output power density and wake characteristics of a downstream turbine. This process can be iteratively applied to predict the wake and output power of each subsequent turbine in a wind farm, supporting the identification of optimal inter-turbine spacing. The proposed method is illustrated using a utility-scale 5 MW wind turbine. The results show that the errors of predicted output power for a stand-alone wind turbine and multiple wind turbines are blew 3 %.

中文翻译：

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使用大涡模拟和卷积神经网络预测海上风力涡轮机尾流和输出功率


预测海上风力涡轮机尾流和输出功率对于优化风电场布局和最大限度地提高风能产量至关重要。近年来，已经开发了几种计算流体动力学方法来预测风力涡轮机尾流和输出功率，与传统分析模型相比，它们表现出了良好的性能。然而，计算流体动力学在海上风电场设计中通常涉及高计算成本，因为具有不同涡轮机间距的涡轮机需要考虑各种海上风电条件。为了确保预测海上风力涡轮机尾流和输出功率的保真度和效率，本研究使用了大涡模拟和卷积神经网络。大涡模拟有效地集成了致动器线法和离散化和合成随机流生成，以生成不同入射风速和湍流强度下独立涡轮机的尾流速度、尾流湍流强度和输出功率。使用生成的数据集，卷积神经网络可以有效地捕获独立涡轮机的输入和输出之间的关系。然后，涡轮机的预测尾流数据可以用作输入，以估计下游涡轮机的输出功率密度和尾流特性。此过程可以迭代应用来预测风电场中每个后续涡轮机的尾流和输出功率，从而支持确定最佳涡轮机间距。使用公用事业规模的 5 MW 风力涡轮机来说明所提出的方法。结果表明，独立风力涡轮机和多个风力涡轮机的预测输出功率误差吹低了 3 %。