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Uncertainties of the 50-year wind from short time series using generalized extreme value distribution and generalized Pareto distribution

机译:使用广义极值分布和广义Pareto分布的短时间序列的50年风的不确定性

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摘要

This study examines the various sources to the uncertainties in the application of two widely used extreme value distribution functions, the generalized extreme value distribution (GEVD) and the generalized Pareto distribution (GPD). The study is done through the analysis of measurements from several Danish sites, where the extreme winds are caused by the Atlantic lows. The simple extreme wind mechanism here helps us to focus on the issues mostly related to the use of limited wind measurements. Warnings are flagged and possible solutions are discussed. Thus, this paper can be used as a guideline for applying GEVD and GPD to wind time series of limited length. The data analysis shows that, with reasonable choice of relevant parameters, GEVD and GPD give consistent estimates of the return winds. For GEVD, the base period should be chosen in accordance with the occurrence of the extreme wind events of the same mechanism. For GPD, the choices of the threshold, the definition of independent samples and the shape factor are interrelated. It is demonstrated that the lack of climatological representativity is a major source of uncertainty to the use of both GEVD and GPD; the information of climatological variability is suggested to be extracted from global or mesoscale models.
机译:这项研究探讨了两种广泛使用的极值分布函数(广义极值分布(GEVD)和广义帕累托分布(GPD))在应用中的不确定性的各种来源。这项研究是通过分析几个丹麦站点的测量结果而完成的,这些站点的极端风是由大西洋低气压引起的。这里的简单极端风机制可以帮助我们集中精力解决与有限风速测量的使用有关的问题。标记了警告,并讨论了可能的解决方案。因此,本文可以作为将GEVD和GPD应用于有限长度的风时间序列的指南。数据分析表明,通过合理选择相关参数,GEVD和GPD可以得出一致的回风估算值。对于GEVD,应根据相同机构的极端风事件的发生来选择基准期。对于GPD,阈值的选择,独立样本的定义和形状因子是相互关联的。事实证明,缺乏气候代表性是使用GEVD和GPD的不确定性的主要来源。建议从全局或中尺度模型中提取气候变化信息。

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