Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/75310
Título: | A global sensitivity analysis method applied to wind farm power output estimation models | Autores/as: | Carta González, José Antonio Díaz, Santiago Castañeda, A. |
Clasificación UNESCO: | 330607 Maquinaria rotatoria 332205 Fuentes no convencionales de energía 332201 Distribución de la energía |
Palabras clave: | Ensemble Technique Global Sensitivity Analysis Shapley Effects Sobol' Indices Vine Copula Modelling, et al. |
Fecha de publicación: | 2020 | Proyectos: | Adaptación Al Cambio Climático de Los Sistemas Energéticos de la Macaronesia | Publicación seriada: | Applied Energy | Resumen: | This paper proposes a global sensitivity analysis method applied to wind farm power output estimation models. The relevance of a global sensibility analysis is that it allows quantification of the contribution of the uncertainty of each input variable of the estimation model to the uncertainty of the response of the model. Measures of sensitivity based on Sobol' indices have been used in the field of energy. Sobol’ indices are constructed based on the assumption that the model input variables are statistically independent. The method proposed in this paper uses Shapley effects and a regular vine copula to take into account the probable dependency among the input variables of the models. The model used as case study is fed with sixteen meteorological variables and six operational variables. The following are some of the most important results obtained in the case study: a) Regular vine copulas were more suitable than the drawable vine and canonical vine subclass copulas to simulate the structure of dependency between the random input variables of the used wind farm power output estimation model; b) the Shapley effects were able to overcome the difficulty of interpretation presented by the Sobol' indices with respect to correlations among the input variables of the wind farm power output estimation model. In the case study, the wind speed, active power set-point and turbulence intensity variables explained 98.58% of the variance of the response of the model. The wind direction, nacelle orientation and air density variables only explained 1.42% of that variance. | URI: | http://hdl.handle.net/10553/75310 | ISSN: | 0306-2619 | DOI: | 10.1016/j.apenergy.2020.115968 | Fuente: | Applied Energy[ISSN 0306-2619],v. 280 |
Colección: | Artículos |
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