Efficient and accurate wake models are required for wind plant performance modeling and the suite of engineering processes that support wind plant layout, control, and monitoring. Although many analytical and engineering wake models with low computational costs have been proposed, their ability to represent the power production of large wind plants in a wide range of atmospheric conditions is not completely understood. The following validation study reviews the underlying theory for analytical wake models, outlines quality control procedures for observational data, and compares model results with observational data from the Lillgrund Wind Plant. Lillgrund makes a valuable case study for wake modeling because of its regular arrangement and the relatively close spacing of constituent wind turbines, which lead to regular and significant wake interactions within the wind plant and the development of deep array flow conditions. Formulations for the velocity deficit, wake-added turbulence, and wake superposition methods are considered in a modular sense, yielding many possible configurations to represent wind turbine wakes, of which seven are examined in detail. Velocity deficit models that account for flow conditions in the near wake are better able to reproduce power production for wind turbines in the transitional region of the wind plant, where wind turbines experience as many as five wakes from upstream turbines. In the deep array, where power production reaches asymptotic values and wake statistics become quasi-periodic, wake superposition schemes become the largest driver in error reduction; using the linear or maximum wake superposition methods can reduce the relative root mean square error by as much as 40% in the deep array.

中文翻译：

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模块化分析尾流模型与 Lillgrund 风力发电厂的比较

风电厂性能建模和支持风电厂布局、控制和监测的工程流程套件需要高效且准确的尾流模型。尽管已经提出了许多具有低计算成本的分析和工程尾流模型，但它们代表大型风力发电厂在各种大气条件下发电的能力尚未完全了解。以下验证研究回顾了分析尾流模型的基本理论，概述了观测数据的质量控制程序，并将模型结果与来自 Lillgrund 风力发电厂的观测数据进行了比较。Lillgrund 为尾流建模提供了一个有价值的案例研究，因为它有规律的排列和组成风力涡轮机的相对较近的间距，这导致风电厂内定期和显着的尾流相互作用以及深阵列流条件的发展。在模块化意义上考虑速度赤字、尾流附加湍流和尾流叠加方法的公式，产生许多可能的配置来表示风力涡轮机尾流，其中详细检查了其中的七个。解释近尾流中流动条件的速度赤字模型能够更好地再现风力发电厂过渡区域中风力涡轮机的发电量，在那里风力涡轮机经历多达五个来自上游涡轮机的尾流。在深度阵列中，当功率产生达到渐近值并且尾流统计成为准周期性时，尾流叠加方案成为减少误差的最大驱动因素；