Open AccessUncertainties of the 50‐year wind from short time series using generalized extreme value distribution and generalized Pareto distribution
Author(s) -
Larsén Xiaoli Guo,
Mann Jakob,
Rathmann Ole,
Jørgensen Hans E.
Publication year - 2015
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.1683
Subject(s) - extreme value theory , generalized pareto distribution , generalized extreme value distribution , gumbel distribution , wind power , meteorology , mesoscale meteorology , wind speed , econometrics , series (stratigraphy) , environmental science , distribution (mathematics) , climatology , statistics , mathematics , geography , engineering , geology , paleontology , electrical engineering , mathematical analysis
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. © 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.