Version 1
: Received: 8 December 2021 / Approved: 10 December 2021 / Online: 10 December 2021 (14:03:52 CET)
How to cite:
Chiodo, E.; Fantauzzi, M.; Mazzanti, G. The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation. Preprints2021, 2021120187. https://doi.org/10.20944/preprints202112.0187.v1
Chiodo, E.; Fantauzzi, M.; Mazzanti, G. The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation. Preprints 2021, 2021120187. https://doi.org/10.20944/preprints202112.0187.v1
Chiodo, E.; Fantauzzi, M.; Mazzanti, G. The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation. Preprints2021, 2021120187. https://doi.org/10.20944/preprints202112.0187.v1
APA Style
Chiodo, E., Fantauzzi, M., & Mazzanti, G. (2021). The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation. Preprints. https://doi.org/10.20944/preprints202112.0187.v1
Chicago/Turabian Style
Chiodo, E., Maurizio Fantauzzi and Giovanni Mazzanti. 2021 "The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation" Preprints. https://doi.org/10.20944/preprints202112.0187.v1
Abstract
The paper deals with the Compound Inverse Rayleigh distribution, shown to constitute a proper model for the characterization of the probability distribution of extreme values of wind-speed, a topic which is gaining growing interest in the field of renewable generation assessment, both in view of wind power production evaluation and the wind-tower mechanical reliability and safety. The first part of the paper illustrates such model starting from its origin as a generalization of the Inverse Rayleigh model - already proven to be a valid model for extreme wind-speeds - by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity to interpret different field data is illustrated, also by means of numerous numerical examples based upon real wind speed measurements. Then, a novel Bayes approach for the estimation of such extreme wind-speed model is proposed. The method relies upon the assessment of prior information in a practical way, that should be easily available to system engineers. In practice, the method allows to express one’s prior beliefs both in terms of parameters, as customary, and/or in terms of probabilities. The results of a large set of numerical simulations – using typical values of wind-speed parameters - are reported to illustrate the efficiency and the accuracy of the proposed method. The validity of the approach is also verified in terms of its robustness with respect to significant differences compared to the assumed prior information.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
