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Comparing wind generation profiles: A circular data approach

机译：比较风力发电剖面：循环数据方法

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The importance of wind power energy for energy and environmental policies has been growing in past recent years. However, because of its random nature over time, the wind generation cannot be reliable dispatched and perfectly forecasted, becoming a challenge when integrating this production in power systems. In addition the wind energy has to cope with the diversity of production resulting from alternative wind power profiles located in different regions. In 2012, Portugal presented a cumulative installed capacity distributed over 223 wind farms [1]. In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour(s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese system.In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour(s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese sys- em.
机译：近年来，风能对能源和环境政策的重要性日益提高。但是，由于其随时间变化的随机性，因此无法可靠地调度和完美预测风力发电，这在将这种发电方式集成到电力系统中时成为一个挑战。此外，风能还必须应对由于位于不同区域的替代风能剖面而导致的生产多样性。 2012年，葡萄牙展示了223个风电场的累计装机容量[1]。在这项工作中，使用循环数据统计方法来分析和比较替代性空间风力生成剖面。分析指示极端情况的变量。考虑当天农场的产量达到每日最大产量的小时。该变量已转换为循环变量，使用循环统计信息可以确定不同风力发电量的每日小时分布。将此方法应用于实际案例，考虑葡萄牙电力系统提供的有关2012年的数据，间隔为15分钟。考虑了六个地理位置，代表了葡萄牙系统中不同的风力发电剖面。在这项工作中，使用了循环数据统计方法来分析和比较替代性的空间风力发电剖面。分析指示极端情况的变量。考虑当天农场的产量达到每日最大产量的小时。该变量已转换为循环变量，使用循环统计信息可以确定不同风力发电量的每日小时分布。考虑到葡萄牙电力系统提供的有关2012年的数据（间隔为15分钟），该方法已应用于实际案例。考虑了六个地理位置，代表了葡萄牙系统中不同的风力发电剖面。

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