Abstract The relationship between wind power ramp events and their causative weather systems remains poorly understood, despite its importance to the development of ramp forecasting procedures. Results from previous studies linking ramp events and weather systems have proven difficult to generalize and methodologies used may be difficult to duplicate, especially in cases of measured data scarcity. Accordingly, this paper proposes a flexible methodology for investigating this link between ramps and weather systems in instances of measured data scarcity. A historic wind power time-series is firstly simulated by applying stochastic variations to numeric weather prediction (NWP) reanalysis data. Ramps events are identified within the time-series using a swinging door algorithm. Temporal regularities in ramp statistics are identified as these provide probabilistic insights into ramp occurrences. Finally, ramps are linked to a set of atmospheric circulation archetypes. These archetypes are identified by applying self-organizing maps as a classification procedure to historic NWP data. The proposed methodology is demonstrated through a case study considering a wind farm in South Africa. It is found that mean power and power variability differ significantly as a function of atmospheric circulation, and that thermally driven land-sea breeze interaction can be a primary mechanism for ramp events.

中文翻译：

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风力发电坡道的模拟和检测及其成因大气环流模式的识别

摘要 风电斜坡事件与其成因天气系统之间的关系仍然知之甚少，尽管它对斜坡预测程序的发展很重要。先前将斜坡事件和天气系统联系起来的研究结果已证明难以概括，所使用的方法可能难以复制，尤其是在测量数据稀缺的情况下。因此，本文提出了一种灵活的方法，用于在测量数据稀缺的情况下调查匝道和天气系统之间的这种联系。首先通过将随机变化应用于数值天气预报 (NWP) 再分析数据来模拟历史风电时间序列。斜坡事件使用摆门算法在时间序列内识别。匝道统计中的时间规律被识别，因为它们提供了对匝道发生的概率洞察。最后，坡道与一组大气环流原型相关联。这些原型是通过将自组织地图应用为历史 NWP 数据的分类程序来识别的。通过考虑南非风电场的案例研究证明了所提出的方法。发现作为大气环流的函数，平均功率和功率变化有显着差异，并且热驱动的陆海风相互作用可能是斜坡事件的主要机制。通过考虑南非风电场的案例研究证明了所提出的方法。发现作为大气环流的函数，平均功率和功率变化有显着差异，并且热驱动的陆海风相互作用可能是斜坡事件的主要机制。通过考虑南非风电场的案例研究证明了所提出的方法。发现作为大气环流的函数，平均功率和功率变化有显着差异，并且热驱动的陆海风相互作用可能是斜坡事件的主要机制。