Wind farm multiple aggregated power curve modeling plays an important role in reducing the complexity of analyses in wind farm management and annual power prediction. There is a trade-off between the complexity and accuracy of aggregated power curves. In this paper, K -Means clustering is utilized to classify turbines in a wind farm into homogeneous groups according to a new set of features based on the overall performance of turbines. We apply multilevel modeling methods, including random intercept and random slope models on turbine clusters, to take into account the hidden correlation among different clusters. Results show that the accuracy of our proposed methods are higher than the single aggregated method alongside an equal complexity. The proposed multiple aggregated power curve model can be utilized to analyze wind farm behavior and wind farm power simulations to forecast wind power.

中文翻译：

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风电场聚合功率曲线的多级建模方法


风电场多聚合功率曲线建模对于降低风电场管理和年功率预测分析的复杂性起着重要作用。聚合功率曲线的复杂性和准确性之间存在权衡。在本文中，K 均值聚类用于根据基于涡轮机整体性能的一组新特征将风电场中的涡轮机分为同质组。我们应用多级建模方法，包括涡轮机集群上的随机截距和随机斜率模型，以考虑不同集群之间的隐藏相关性。结果表明，我们提出的方法的准确性高于单一聚合方法，同时复杂性相同。所提出的多聚合功率曲线模型可用于分析风电场行为和风电场功率模拟以预测风电。