Predicting the occurrence of strong and sudden variations in wind power, so‐called ramp events, has become one of the main challenges for the operation of power systems with large shares of wind power. In this paper, we investigate 14 ramp events of different magnitudes and minute‐scale durations observed by a dual‐Doppler radar system at the Westermost Rough offshore wind farm. The identified ramps are characterised using radar observations, turbine data and data from the Weather Research and Forecasting (WRF) model. A remote sensing‐based forecasting methodology that propagates wind speeds upstream of wake‐free turbines is extended here to the whole farm, by including corrections for wake effects. The methodology aims to probabilistically forecast the wind turbines' power in the form of density forecasts. The ability to predict ramp events of different magnitudes is evaluated and compared with probabilistic statistical and physical benchmarks. During the observed ramp events, the remote sensing‐based forecasting model strongly outperforms the benchmarks. We show here that remote sensing observations such as radar data can significantly enhance very short‐term forecasts of wind power.

中文翻译：

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使用双多普勒雷达的海上风力发电机功率斜坡的微小尺度检测和概率预测

预测风力发电的剧烈变化和突然变化，即所谓的斜坡事件，已经成为拥有大量风力发电的电力系统运行的主要挑战之一。在本文中，我们调查了在Westermost Rough海上风电场使用双多普勒雷达系统观测到的14个不同幅度和微小尺度持续时间的斜坡事件。使用雷达观测，涡轮机数据和来自气象研究与预报（WRF）模型的数据对已识别的坡道进行特征化。通过对无尾流涡轮机上游传播风速的基于遥感的预测方法，在此通过包括对无尾流影响的校正，扩展到了整个农场。该方法旨在以密度预测的形式概率地预测风力涡轮机的功率。评估了预测不同幅度的斜坡事件的能力，并将其与概率统计和物理基准进行比较。在观测到的斜坡事件期间，基于遥感的预测模型大大优于基准。我们在这里表明，诸如雷达数据之类的遥感观测可以显着增强非常短期的风能预报。