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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Elio Chiodo & Maurizio Fantauzzi & Giovanni Mazzanti, 2022. "The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and Its Bayes Estimation," Energies, MDPI, vol. 15(3), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:861-:d:733265
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    References listed on IDEAS

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    1. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
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    Cited by:

    1. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.
    2. Elio Chiodo & Bassel Diban & Giovanni Mazzanti & Fabio De Angelis, 2023. "A Review on Wind Speed Extreme Values Modeling and Bayes Estimation for Wind Power Plant Design and Construction," Energies, MDPI, vol. 16(14), pages 1-20, July.

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