The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and Its Bayes Estimation

Elio Chiodo, Maurizio Fantauzzi and Giovanni Mazzanti
Additional contact information
Elio Chiodo: Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Maurizio Fantauzzi: Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Giovanni Mazzanti: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy

Energies, 2022, vol. 15, issue 3, 1-26

Abstract: This paper proposes the Compound Inverse Rayleigh distribution as a proper model for the characterization of the probability distribution of extreme values of wind-speed. This topic is gaining interest in the field of renewable generation, from the viewpoint of assessing both wind power production and 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 by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity for interpreting different field data is illustrated resorting to real wind speed data. 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. 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.

Keywords: renewable energy; bayes estimation; beta distribution; lognormal distribution; compound inverse Rayleigh distribution; extreme values; safety; wind power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/3/861/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/3/861/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:3:p:861-:d:733265

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().