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A Review on Wind Speed Extreme Values Modeling and Bayes Estimation for Wind Power Plant Design and Construction

Author

Listed:
  • Elio Chiodo

    (Department of Industrial Engineering, University of Naples Federico II, 80125 Napoli, Italy)

  • Bassel Diban

    (Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, 40136 Bologna, Italy)

  • Giovanni Mazzanti

    (Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, 40136 Bologna, Italy)

  • Fabio De Angelis

    (Department of Structures for Engineering and Architecture, University of Naples Federico II, 80125 Napoli, Italy)

Abstract

Rapid growth of the use of wind energy calls for a more careful representation of wind speed probability distribution, both for identification and estimation purposes. In particular, a key point of the above identification and estimation aspects is representing the extreme values of wind speed probability distributions, which are of great interest both for wind energy applications and structural tower reliability analysis. The paper reviews the most adopted probability distribution models and estimation methods. In particular, for reasons which are properly discussed, attention is focused on the evaluation of an opportune “safety index” related to extreme values of wind speeds or gusts. This topic has gained increasing attention in recent years in both wind energy generation assessment and also in risk and structural reliability and safety analysis. With regard to wind energy generation, there is great sensitivity in the relationship between wind speed extreme upper quantiles and the corresponding wind energy quantiles. Concerning the risk and reliability analysis of structures, extreme wind speed value characterization is useful for a proper understanding of the destructive wind forces that may affect structural tower reliability analysis and, consequently, the proper choice of the cut off wind speed value; therefore, the above two kinds of analyses are somewhat related to each other. The focus is on the applications of the Bayesian inference technique for estimating the above safety index due to its effectiveness and usefulness.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5456-:d:1196546
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    References listed on IDEAS

    as
    1. Celik, A.N., 2006. "A simplified model for estimating yearly wind fraction in hybrid-wind energy systems," Renewable Energy, Elsevier, vol. 31(1), pages 105-118.
    2. 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.
    3. Sloughter, J. McLean & Gneiting, Tilmann & Raftery, Adrian E., 2010. "Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 25-35.
    4. Takvor Soukissian & Christos Tsalis, 2015. "The effect of the generalized extreme value distribution parameter estimation methods in extreme wind speed prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1777-1809, September.
    5. Bashir Ahmed Albashir Abdulali & Mohd Aftar Abu Bakar & Kamarulzaman Ibrahim & Noratiqah Mohd Ariff & Alessandro De Gregorio, 2022. "Extreme Value Distributions: An Overview of Estimation and Simulation," Journal of Probability and Statistics, Hindawi, vol. 2022, pages 1-17, October.
    6. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    7. Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
    8. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
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