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Classification of wind speed distributions using a mixture of Dirichlet distributions

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  • Calif, Rudy
  • Emilion, Richard
  • Soubdhan, Ted

Abstract

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. The high variability of this renewable energy source can caused a disruptive effect on power quality and reliability, in non-interconnected island networks as in Guadeloupe (French West Indies). To palliate these difficulties, it is essential to identify and characterize the wind speed distribution over very short time intervals. This allows anticipating 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 distributions by estimating a finite mixture of Dirichlet distributions. The SAEM algorithm that we use provides a fine distinction between three wind speed distribution classes. It is a new nonparametric method for wind speed classification.

Suggested Citation

  • Calif, Rudy & Emilion, Richard & Soubdhan, Ted, 2011. "Classification of wind speed distributions using a mixture of Dirichlet distributions," Renewable Energy, Elsevier, vol. 36(11), pages 3091-3097.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:11:p:3091-3097
    DOI: 10.1016/j.renene.2011.03.024
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    Cited by:

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    6. Zárate-Miñano, Rafael & Milano, Federico, 2016. "Construction of SDE-based wind speed models with exponentially decaying autocorrelation," Renewable Energy, Elsevier, vol. 94(C), pages 186-196.

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