风电场作为传感器阵列的湍流边界层时空流动结构
Wind farms as sensor arrays of turbulent boundary layer spatio-temporal flow structure
AI总结：
本文提出扩展分析框架预测风电场功率波动谱，结合LES验证，可准确预测总功率频谱，助力风电场电网整合。
AI中文摘要：
风电场发电量的时间波动源于大气湍流、涡轮机特性及风电场布局之间的相互作用，然而准确表征这些波动仍是一个未解决的挑战。本文提出并扩展了一个分析框架，用于预测风电场功率波动的时间谱，并将其预测结果与常规中性边界层内运行的风电场的详细大涡模拟（LES）结果进行了比较。该建模框架整合了湍流边界层物理学中的多个成熟概念：考虑平均平流并假设大涡随机扫掠的时空湍流谱模型、提供所需平均流和湍流尺度的完全发展涡轮机阵列边界层流动的自上而下风电场模型，以及表示涡轮机位置和有限转子尺寸的空间采样核。后者被扩展为三维形式，以表示沿垂直方向对湍流空间波动的滤波作用。仅利用大气、涡轮机和布局参数，该模型的预测结果与平坦地形上大型风电场的广泛LES数据库进行了评估。该模型能够准确预测总功率频谱，包括与涡轮机列之间平流相关的峰值、由于转子平均作用导致的惯性范围湍流波动的衰减，以及阵列内不同涡轮机组（如交错或随机子集）的总功率信号频谱。这种从基础流体动力学和现有边界层湍流模型预测风力发电波动谱的能力，有望助力改善风电场的电网整合。
英文摘要：
Temporal fluctuations of wind farm-generated power arise from the interaction between atmospheric turbulence, turbine properties, and wind-farm layout. However, accurately characterizing these fluctuations remains an open challenge. We here present and extend an analytical framework to predict the temporal spectrum of wind farm power-fluctuations, and compare its predictions with detailed large-eddy-simulations (LES) of a wind farm operating within a conventionally neutral boundary layer. The modeling framework assembles several established concepts from turbulent boundary layer physics: a spatio-temporal turbulence spectral model accounting for mean advection and assuming random sweeping by large eddies, a top-down wind farm model of a fully developed wind turbine array boundary layer flow providing the required mean-flow and turbulence scales, and a spatial sampling kernel representing turbine positions and finite rotor size. The latter is extended to three dimensions to represent filtering of spatial fluctuations of turbulence along the vertical direction. Using only atmospheric, turbine, and layout parameters, the model predictions are evaluated against an extensive LES database of a large wind farm on flat terrain. The model accurately predicts the aggregate power frequency spectrum, including peaks associated with advection between turbine rows, the decay of inertial-range turbulence fluctuations due to rotor averaging, and spectra of aggregate power signals from various arrangements of groups of turbines within the array (e.g. staggered or random subsets). The ability to predict wind power fluctuation spectra from fundamental fluid dynamics and existing boundary layer turbulence models could help improve wind farm grid integration.
