English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Wind energy production is very sensitive to instantaneous wind speedfluctuations. Thus rapid variation of wind speed due to changes in the localmeteorological conditions can lead to electrical power variations of the orderof the nominal power output. In small grids, as they exist for example on someislands in the French West Indies, such fluctuations can cause instabilities incase of intermediate power shortages. To palliate these difficulties, it isessential to identify and characterize the wind speed distributions. Thisallows to anticipate the eventuality of power shortage or power surge.Therefore, it is of interest to categorize wind speed fluctuations intodistinct classes and to estimate the probability of a distribution to belong toa class. This paper presents a method for classifying wind speed histograms byestimating a finite mixture of Dirichlet distributions. The SAEM algorithm thatwe 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 tospecific Gram- Charlier densities.

Wind speed classification using Dirichlet mixtures