The estimation of wind speed extreme values is a topic assuming increasing importance in wind energy studies. Two different methods are compared here for that purpose, in view of safety applications. The first model is a parametric one, based upon a classical extreme value model, such as the Gumbel or the Inverse Weibull distribution. The alternative model is a "non parametric" one, based upon a stochastic characterization of the wind speed by means of a Poisson distribution. For both methods, estimates are carried out by means of Bayes estimation approach. The two approaches are compared in terms of robustness of the estimates of a proper safety index, with respect to departures from the assumed wind speed model. A large set of simulations results are discussed, as a first step towards a deeper insight to wind speed estimation methods, taking into account model uncertainty.
E. Chiodo, G. Mazzanti, M. Karimian, R. Zoh (2015). Comparison of two different estimation methods of wind speed extreme values. Piscataway, New Jersey : IEEE [10.1109/ICCEP.2015.7177589].
Comparison of two different estimation methods of wind speed extreme values
MAZZANTI, GIOVANNI;
2015
Abstract
The estimation of wind speed extreme values is a topic assuming increasing importance in wind energy studies. Two different methods are compared here for that purpose, in view of safety applications. The first model is a parametric one, based upon a classical extreme value model, such as the Gumbel or the Inverse Weibull distribution. The alternative model is a "non parametric" one, based upon a stochastic characterization of the wind speed by means of a Poisson distribution. For both methods, estimates are carried out by means of Bayes estimation approach. The two approaches are compared in terms of robustness of the estimates of a proper safety index, with respect to departures from the assumed wind speed model. A large set of simulations results are discussed, as a first step towards a deeper insight to wind speed estimation methods, taking into account model uncertainty.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.