Quantification and reduction of uncertainties in a wind turbine numerical model based on global sensitivity analysis and recursive Bayesian inference approach

Adrien Hirvoas (1, 2) , Clémentine Prieur (2) , Élise Arnaud (2) , Fabien Caleyron (1) , Miguel Munoz Zuniga (1)

1 IFPEN - IFP Energies nouvelles
2 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows

A framework to perform quantification and reduction of uncertainties in a wind turbine numerical model using global sensitivity analysis and recursive Bayesian inference method is developed in this paper. We explain how a prior probability distribution on the model parameters is transformed into a posterior probability distribution, by incorporating a physical model and real field noisy observations. Nevertheless, these approaches suffer from the so-called curse of dimensionality. In order to reduce the dimension, Sobol' indices approach for global sensitivity analysis, in the context of wind turbine modelling, is presented. A major issue arising for such inverse problems is identifiabil-ity, i.e. whether the observations are sufficient to unambiguously determine the input parameters that generated the observations. Hereafter, global sensitivity analysis is also used in the context of identifiability.

Keywords: Inference; uncertainty quantification; model calibration; nonlinear Kalman filters; Global Sensitivity Analysis; Wind turbine monitoring

Domains: Statistics [math.ST]

Adrien Hirvoas, Clémentine Prieur, Élise Arnaud, Fabien Caleyron, Miguel Munoz Zuniga. Quantification and reduction of uncertainties in a wind turbine numerical model based on global sensitivity analysis and recursive Bayesian inference approach. 2020. ⟨hal-03279947v1⟩