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Forecast of icing events at a wind farm in Sweden

机译:预测瑞典风电场的结冰事件

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摘要

This paper introduces a method for identifying icing events using a physical icing model, driven by atmospheric data from the Weather Research and Forecasting (WRF) model, and applies it to a wind park in Sweden. Observed wind park icing events were identified by deviation from an idealized power curve and observed temperature. The events were modeled using a physical icing model with equations for both accretion and ablation mechanisms (iceBlade). The accretion model is based on the Makkonen model but was modified to make it applicable to the blades of a wind turbine rather than a static structure, and the ablation model is newly developed. The results from iceBlade are shown to outperform a 1-day persistence model and standard cylinder model in determining the times when any turbine in the wind park is being impacted by icing. The icing model was evaluated using inputs from simulations using nine different WRF physics parameterization combinations. The combination of the Thompson microphysics parameterization and version 2 of the Mellor-Yamada-Nakanishi-Niino PBL scheme was shown to perform best at this location. The distribution of cloud mass into the appropriate hydrometeor classes was found to be very important for forecasting the correct icing period. One concern with the iceBlade approach was the relatively high false alarm rates at the end of icing events due to the ice not being removed rapidly enough. © 2014 American Meteorological Society.
机译:本文介绍了一种基于物理结冰模型的结冰事件识别方法,该方法由来自气象研究与预报(WRF)模型的大气数据驱动,并将其应用于瑞典的风电场。观测到的风电场结冰事件是通过偏离理想功率曲线和观测温度来确定的。使用物理结冰模型对事件进行建模,并使用方程式确定吸积和消融机制(iceBlade)。吸积模型基于Makkonen模型,但对其进行了修改以使其适用于风力涡轮机的叶片而非静态结构,并且消融模型是最新开发的。在确定风场中任何涡轮机结冰的时间时,iceBlade的结果显示优于1天持久性模型和标准气缸模型。使用来自九个不同WRF物理参数化组合的模拟输入来评估结冰模型。汤普森微物理参数化和Mellor-Yamada-Nakanishi-Niino PBL方案的版本2的组合在此位置显示出最佳性能。发现将云团分布到适当的水凝物类别中对于预测正确的结冰期非常重要。 iceBlade方法的一个问题是在结冰事件结束时相对较高的误报率,这是因为冰的移除速度不够快。 ©2014美国气象学会。

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