Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Wind energy production is very sensitive to turbulent wind speed. Thus rapid variation of wind speed due to changes in the local meteorological conditions can lead to electrical power variations of the order of the nominal power output, in particular when wind power variations on very short time scales, range at few seconds to 1 h, are considered. In small grid as they exist on islands (Guadeloupean Archipelago: French West Indies) such fluctuations can cause instabilities in case of intermediate power shortages. The developed analysis in [14] reveals three main classes of time series for the wind speed fluctuations. In this work, Probability Density Functions (PDFs) are proposed to fit the wind speed fluctuations distributions in each class. After, to simulate wind speed fluctuations sequences, we use a stochastic differential equation, the Langevin equation considering Gaussian turbulence PDF (class I), Gram–Charlier PDF (class II) and a mixture of gaussian PDF (class III). The statistical and dynamical properties of simulated wind sequences are close to those of measured wind sequences, for each class
ISSN: 0306-2619

hal-01527885
https://hal.archives-ouvertes.fr/hal-01527885
DOI : 10.1016/j.apenergy.2012.05.007

Éditeur(s) : HAL CCSD
Résumé : 32 pages
Wind energy production is very sensitive to instantaneous wind speed fluctuations. Thus rapid variation of wind speed due to changes in the local meteorological conditions can lead to electrical power variations of the order of the nominal power output. In small grids, as they exist for example on some islands in the French West Indies, such fluctuations can cause instabilities in case of intermediate power shortages. To palliate these difficulties, it is essential to identify and characterize the wind speed distributions. This allows to anticipate the eventuality of power shortage or power surge. Therefore, it is of interest to categorize wind speed fluctuations into distinct classes and to estimate the probability of a distribution to belong to a class. This paper presents a method for classifying wind speed histograms by estimating a finite mixture of Dirichlet distributions. The SAEM algorithm that we use provides a fine distinction between wind speed distribution classes. It's a new nonparametric method for wind speed sequences classification. However, we show that the wind speed distributions in each class correspond to specific Gram- Charlier densities.
https://hal.archives-ouvertes.fr/hal-00319608

hal-00319608
https://hal.archives-ouvertes.fr/hal-00319608
https://hal.archives-ouvertes.fr/hal-00319608v2/document
https://hal.archives-ouvertes.fr/hal-00319608/file/Article_dirichlet.pdf
ARXIV : 0810.4422