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A statistical methodology for the estimation of extreme wave conditions for offshore renewable applications

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  • Larsén, Xiaoli Guo
  • Kalogeri, Christina
  • Galanis, George
  • Kallos, George

Abstract

Accurate estimation of extreme wave conditions is critical for offshore renewable energy activities and applications. The use of numerical wind and wave models gives a credible and convenient way of monitoring the general atmospheric and sea state conditions, especially in the absence of sufficient observational networks. However, when focusing on the study of non-frequent cases, in particular over coastal areas, increased uncertainty in the model outputs and accordingly in the reliability of the estimation of extreme waves becomes an important issue. The current study introduces a methodology to validate and post-process outputs from a high resolution numerical wave modeling system for extreme wave estimation based on the significant wave height. This approach is demonstrated through the data analysis at a relatively deep water site, FINO 1, as well as a relatively shallow water area, coastal site Horns Rev, which is located in the North Sea, west of Denmark. The post-processing targets at correcting the modeled time series of the significant wave height, in order to match the statistics of the corresponding measurements, including not only the conventional parameters such as the mean and standard deviation, but also a new parameter, the second-order spectral moment. This second-order spectral moment is essential for extreme value estimation but has so far been neglected in relevant studies. The improved model results are utilized for the estimation of the 50-year values of significant wave height as a characteristic index of extreme wave conditions. The results from the proposed methodology seem to be in a good agreement with the measurements at both the relatively deep, open water and the shallow, coastal water sites, providing a potentially useful tool for offshore renewable energy applications.

Suggested Citation

  • Larsén, Xiaoli Guo & Kalogeri, Christina & Galanis, George & Kallos, George, 2015. "A statistical methodology for the estimation of extreme wave conditions for offshore renewable applications," Renewable Energy, Elsevier, vol. 80(C), pages 205-218.
    • Handle: RePEc:eee:renene:v:80:y:2015:i:c:p:205-218
    DOI: 10.1016/j.renene.2015.01.069
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    References listed on IDEAS

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    1. Arno Behrens & Heinz Günther, 2009. "Operational wave prediction of extreme storms in Northern Europe," 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. 49(2), pages 387-399, May.
    2. Wimmer, Werenfrid & Challenor, Peter & Retzler, Chris, 2006. "Extreme wave heights in the North Atlantic from Altimeter Data," Renewable Energy, Elsevier, vol. 31(2), pages 241-248.
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    Cited by:

    1. Kalogeri, Christina & Galanis, George & Spyrou, Christos & Diamantis, Dimitris & Baladima, Foteini & Koukoula, Marika & Kallos, George, 2017. "Assessing the European offshore wind and wave energy resource for combined exploitation," Renewable Energy, Elsevier, vol. 101(C), pages 244-264.
    2. Gaidai, Oleg & Xu, Xiaosen & Wang, Junlei & Ye, Renchuan & Cheng, Yong & Karpa, Oleh, 2020. "SEM-REV offshore energy site wind-wave bivariate statistics by hindcast," Renewable Energy, Elsevier, vol. 156(C), pages 689-695.
    3. Gaidai, Oleg & Ji, Chunyan & Kalogeri, Christina & Gao, Junliang, 2017. "SEM-REV energy site extreme wave prediction," Renewable Energy, Elsevier, vol. 101(C), pages 894-899.
    4. Kresning, Boma & Hashemi, M. Reza & Shirvani, Amin & Hashemi, Javad, 2024. "Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions," Renewable Energy, Elsevier, vol. 220(C).
    5. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.
    6. Gómez-Orellana, A.M. & Guijo-Rubio, D. & Gutiérrez, P.A. & Hervás-Martínez, C., 2022. "Simultaneous short-term significant wave height and energy flux prediction using zonal multi-task evolutionary artificial neural networks," Renewable Energy, Elsevier, vol. 184(C), pages 975-989.

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