Title :
Wind speed extreme quantiles estimation
Author_Institution :
Dept. of Electr. Eng. & Inf. Technol., Univ. of Naples Federico II, Naples, Italy
Abstract :
Wind speed (WS) probability distribution identification and estimation are the object of an increasing number of studies, especially related to the need of wind energy production evaluation. In this framework, the paper highlights the characterization of extreme WS quantiles, whose values and estimates are very sensitive to the assumed distributional form. This is a crucial issue not only for wind energy production assessment, but also in risk and reliability analysis. For the above purposes, the Lomax model is theoretically deduced and analysed: this model, indeed, well represents the typical “heavy tails” in WS probabilistic distributions arising from field data. A proper Bayes approach for the estimation of both the Lomax survivor function and of the above quantiles is analyzed. A large set of numerical simulations has been performed, and some typical subsets of them are shown to illustrate the efficiency of the estimates, showing excellent results.
Keywords :
power generation reliability; probability; wind power plants; Bayes approach; Lomax model; Lomax survivor function; WS probability distribution identification; numerical simulation; reliability analysis; risk analysis; wind energy production assessment; wind energy production evaluation; wind speed extreme quantile estimation; wind speed probability distribution identification; Analytical models; Maximum likelihood estimation; Numerical models; Reliability; Wind power generation; Wind speed; Bayes estimation; Gamma distribution; Lomax distribution; Wind Power;
Conference_Titel :
Clean Electrical Power (ICCEP), 2013 International Conference on
Conference_Location :
Alghero
Print_ISBN :
978-1-4673-4429-6
DOI :
10.1109/ICCEP.2013.6586944