The wind speeds of each wind turbine generator (WTG) are the basis for establishing an aggregated model of a wind farm for power system transient simulation. However, the commonly obtained operation data of the wind farm do not include the wind speeds of each WTG. Thus, a modeling method for the wind speeds of the WTGs of same moment inside the wind farm was proposed. A wind speed combination model (WSCM) is established via K-means clustering from the sorted field-measured wind speed data, which can provide reasonable wind speeds for each WTG when aggregate modeling the wind farm. Subsequently, an improved two-step clustering method of the WTGs was proposed, by which the WTGs are initially clustered according to whether they enter low voltage ride through (LVRT) mode or not. An “N-1 VS One” aggregated model and the binary search algorithm were used to quickly and correctly predict whether a WTG in the wind farm enters LVRT mode under a grid fault. The case study demonstrates the satisfactory performance of the WSCM and the necessity of clustering the WTGs according to whether they enter or do not enter LVRT mode for obtaining an accurate aggregated model of the wind farm. All the data, models, and programs that are used in this paper can be found at IEEE Dataport, DOI: 10.21227/dqyk-t852.

中文翻译：

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风电场内风速建模，在考虑 LVRT 特性的风电场综合建模中的应用

每个风力涡轮发电机 (WTG) 的风速是建立用于电力系统瞬态仿真的风电场聚合模型的基础。然而，通常获得的风电场运行数据并不包括每个风力发电机组的风速。因此，提出了一种风电场内同一时刻风力发电机组风速的建模方法。风速组合模型（WSCM）是通过对排序后的现场实测风速数据进行K-means聚类建立的，可以在对风电场进行聚合建模时为每个WTG提供合理的风速。随后，提出了一种改进的风力发电机组两步聚类方法，根据是否进入低电压穿越（LVRT）模式对风力发电机组进行初始聚类。使用“N-1 VS One”聚合模型和二分搜索算法，快速准确地预测风电场风电机组在电网故障下是否进入LVRT模式。案例研究证明了 WSCM 的令人满意的性能以及根据是否进入 LVRT 模式对 WTG 进行聚类以获得准确的风电场聚合模型的必要性。本文中使用的所有数据、模型和程序都可以在 IEEE Dataport, DOI: 10.21227/dqyk-t852 中找到。