
Digital
Library of the European Council for Modelling and Simulation 
Title: 
Approaches To Stochastic Modeling Of Wind Turbines 
Authors: 
Migran N. Gevorkyan, Anastasiya V. Demidova, Ivan S.
Zaryadov, Robert A. Sobolewski,
Anna V. Korolkova, Dmitry S. Kulyabov,
Leonid A. Sevastianov 
Published in: 
(2017).ECMS 2017 Proceedings
Edited by: Zita Zoltay
Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes VidovicsDancs, János Péter Rádics European Council for Modeling and Simulation. doi:10.7148/2017 ISBN:
9780993244049/ ISBN:
9780993244056 (CD) 31st European Conference on Modelling and Simulation, Budapest, Hungary, May 23^{rd}
– May 26^{th}, 2017 
Citation
format: 
Migran N. Gevorkyan, Anastasiya V. Demidova, Ivan S.
Zaryadov, Robert A. Sobolewski,
Anna V. Korolkova, Dmitry S. Kulyabov,
Leonid A. Sevastianov (2017). Approaches To
Stochastic Modeling Of Wind Turbines, ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes VidovicsDancs, János Péter Rádics European Council
for Modeling and Simulation. doi:
10.7148/20170622 
DOI: 
https://doi.org/10.7148/20170622 
Abstract: 
Background.
This paper study statistical data gathered from wind turbines located on the
territory of the Republic of Poland. The research is aimed to construct the
stochastic model that predicts the change of wind speed with time. Purpose.
The purpose of this work is to find the optimal distribution for the approximation
of available statistical data on wind speed. Methods. We consider four
distributions of a random variable: LogNormal, Weibull, Gamma and Beta. In order to evaluate the
parameters of distributions we use method of maximum likelihood. To assess the the results of approximation
we use a quantilequantile plot. Results. All the
considered distributions properly approximate the available data. The Weibull distribution shows the best results for the
extreme values of the wind speed. Conclusions. The results of the analysis
are consistent with the common practice of using the Weibull
distribution for wind speed modeling. In the future we plan to compare the
results obtained with a much larger data set as well as to build a stochastic
model of the evolution of the wind speed depending on time. 
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