The wind industry in cold climates has shown strong growth in recent years, but turbine icing in these regions can cause significant energy loss leading to a reduction in reliability of wind energy. Previous studies on estimating wind turbine icing (WTI) generally rely on complex physical models, and many only model the ice growth itself while failing to correlate ice growth with energy loss. It is the estimation of icing-induced energy loss that is critical for power grid management to cope with energy deficits associated with extreme weather conditions. This study focuses on bridging this modeling gap through developing an efficacious methodology for predicting icing-induced energy losses for wind turbines in cold weather events. Specifically, this study uses measurements of 11 WTI events between 2018 and 2020 from a 2.5 MW wind turbine (Eolos site, University of Minnesota) to create a statistical correlation between meteorological conditions and icing-induced energy loss. Meteorological icing parameters generated from a Weather Research and Forecasting simulation are used as inputs to the model. The model is validated against in-situ data for all events, and against two additional 1.65 MW wind turbines for one event (Morris site, University of Minnesota). When comparing average estimated energy loss to measured loss, it shows a relative mean absolute error of 37% at Eolos and 2.9% at Morris (after power curve scaling). The new model is additionally implemented for 30 large-scale wind farms in the Midwest region of the United States for estimation of WTI energy loss. The method proposed in this study enables fast and accurate prediction of WTI energy loss for wind turbines.

近年来，寒冷气候下的风能产业呈现强劲增长，但这些地区的涡轮机结冰会造成大量能量损失，从而降低风能的可靠性。先前关于估算风力涡轮机结冰 (WTI) 的研究通常依赖于复杂的物理模型，而且许多研究仅对冰增长本身进行建模，而未能将冰增长与能量损失相关联。结冰引起的能量损失的估计对于电网管理以应对与极端天气条件相关的能量不足至关重要。本研究的重点是通过开发一种有效的方法来预测寒冷天气事件中风力涡轮机的结冰引起的能量损失，从而弥合这一建模差距。具体而言，本研究使用了 2.2 对 2018 年至 2020 年间 11 次 WTI 事件的测量。5 MW 风力涡轮机（Eolos 站点，明尼苏达大学）在气象条件和结冰引起的能量损失之间建立统计相关性。从天气研究和预测模拟生成的气象结冰参数用作模型的输入。该模型针对所有事件的现场数据进行了验证，并针对一个事件（莫里斯站点，明尼苏达大学）的两个额外的 1.65 MW 风力涡轮机进行了验证。在比较平均估计能量损失与测量损失时，它显示 Eolos 的相对平均绝对误差为 37%，Morris 的相对平均绝对误差为 2.9%（功率曲线缩放后）。新模型还用于美国中西部地区的 30 个大型风电场，用于估算 WTI 能源损失。