Gaussian mixture model for extreme wind turbulence estimation

Abstract. Uncertainty quantification is necessary in wind turbine design due to the random nature of environmental inputs, through which uncertainty structural loads and response under specific situations can be quantified. Specifically, turbulence (described by standard deviation longitudinal speed over a 10 min time duration) has significant impact on extreme fatigue envelope turbine. The parameters (mean are not independent stochastic variables, reliability analysis or therefore requires these correlated parameters. An accurate probabilistic model should established correlation among Compared univariate distributions, theoretical multivariate distributions limited flexible enough from different sites direction sectors. Copula-based models often used for description, but existing parametric copulas may well, limitations copula structures. Gaussian mixture widely applied density estimation clustering many domains, studies have been conducted energy few it In this paper, joint distribution mean duration, calculated 15 years measurement series data. As comparison, Nataf transformation (Gaussian copula) Gumbel compared with terms estimated marginal conditional distributions. then adopted estimate (wind load), could taken as an input ultimate limit state parameter contour associated 50-year return period computed what turbines given IEC 61400-1. able where tail both good accuracy, candidate estimation.