IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v212y2023icp570-588.html
   My bibliography  Save this article

Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions

Author

Listed:
  • Song, Yupeng
  • Sun, Tao
  • Zhang, Zili

Abstract

The fatigue reliability-based design is critical for the structural safety of floating offshore wind turbines (FOWTs). In principle, infinite potential load conditions that the FOWT may experience should be considered in fatigue assessment, while in practice only limited number of load conditions are used, which inevitably induces uncertainty to fatigue estimation. However, to this uncertainty, little attention has been paid. In this study, the fatigue reliability of a FOWT is investigated with a focus on this uncertainty. The C-vine copula method is adopted to model concurrent wind and wave conditions, and the probability-based sampling method is employed to determine the load conditions used for fatigue analysis. This uncertainty is assessed quantitatively via bootstrap method based on numerous simulations. The Gaussian random variable can be used to describe the uncertainty, and the standard variation is proportional to the −0.5 power of the number of load conditions. The fatigue reliability evaluation model of FOWT is established considering this uncertainty, and sensitivity analysis is performed to assess the influence of different random variables. The results indicate that the effect of this uncertainty is comparable to some other uncertainties in certain cases, and should be paid attention to in practice.

Suggested Citation

  • Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.
    • Handle: RePEc:eee:renene:v:212:y:2023:i:c:p:570-588
    DOI: 10.1016/j.renene.2023.05.070
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123006948
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.05.070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    2. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    3. Slot, René M.M. & Sørensen, John D. & Sudret, Bruno & Svenningsen, Lasse & Thøgersen, Morten L., 2020. "Surrogate model uncertainty in wind turbine reliability assessment," Renewable Energy, Elsevier, vol. 151(C), pages 1150-1162.
    4. Li, Xuan & Zhang, Wei, 2020. "Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures," Renewable Energy, Elsevier, vol. 147(P1), pages 764-775.
    5. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    6. Li, Xuan & Zhang, Wei, 2020. "Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions," Renewable Energy, Elsevier, vol. 159(C), pages 570-584.
    7. Chen, Jianbing & Song, Yupeng & Peng, Yongbo & Nielsen, Søren R.K. & Zhang, Zili, 2020. "An efficient rotational sampling method of wind fields for wind turbine blade fatigue analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2170-2187.
    8. Chi-Yu Chian & Yi-Qing Zhao & Tsung-Yueh Lin & Bryan Nelson & Hsin-Haou Huang, 2018. "Comparative Study of Time-Domain Fatigue Assessments for an Offshore Wind Turbine Jacket Substructure by Using Conventional Grid-Based and Monte Carlo Sampling Methods," Energies, MDPI, vol. 11(11), pages 1-17, November.
    9. Micallef, Daniel & Rezaeiha, Abdolrahim, 2021. "Floating offshore wind turbine aerodynamics: Trends and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    10. Chen, Jianbing & Liu, Zenghui & Song, Yupeng & Peng, Yongbo & Li, Jie, 2022. "Experimental study on dynamic responses of a spar-type floating offshore wind turbine," Renewable Energy, Elsevier, vol. 196(C), pages 560-578.
    11. Dong, Wenbin & Moan, Torgeir & Gao, Zhen, 2012. "Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 11-27.
    12. Song, Yupeng & Basu, Biswajit & Zhang, Zili & Sørensen, John Dalsgaard & Li, Jie & Chen, Jianbing, 2021. "Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method," Renewable Energy, Elsevier, vol. 168(C), pages 991-1014.
    13. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Zihao & Dong, Sheng, 2023. "A novel decomposition-based approach for non-stationary hub-height wind speed modelling," Energy, Elsevier, vol. 283(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Song, Yupeng & Basu, Biswajit & Zhang, Zili & Sørensen, John Dalsgaard & Li, Jie & Chen, Jianbing, 2021. "Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method," Renewable Energy, Elsevier, vol. 168(C), pages 991-1014.
    2. Ramezani, Mahyar & Choe, Do-Eun & Heydarpour, Khashayar & Koo, Bonjun, 2023. "Uncertainty models for the structural design of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    3. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    4. Abramic, A. & García Mendoza, A. & Haroun, R., 2021. "Introducing offshore wind energy in the sea space: Canary Islands case study developed under Maritime Spatial Planning principles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    5. Wang, L. & Kolios, A. & Liu, X. & Venetsanos, D. & Rui, C., 2022. "Reliability of offshore wind turbine support structures: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    6. Grant, Elenya & Johnson, Kathryn & Damiani, Rick & Phadnis, Mandar & Pao, Lucy, 2023. "Buoyancy can ballast control for increased power generation of a floating offshore wind turbine with a light-weight semi-submersible platform," Applied Energy, Elsevier, vol. 330(PB).
    7. Ju, Shen-Haw, 2022. "Increasing the fatigue life of offshore wind turbine jacket structures using yaw stiffness and damping," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    8. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    9. Tatsuya Sakurahara & Seyed Reihani & Ernie Kee & Zahra Mohaghegh, 2020. "Global importance measure methodology for integrated probabilistic risk assessment," Journal of Risk and Reliability, , vol. 234(2), pages 377-396, April.
    10. Thapa, Mishal & Missoum, Samy, 2022. "Uncertainty quantification and global sensitivity analysis of composite wind turbine blades," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    11. Derennes, Pierre & Morio, Jérôme & Simatos, Florian, 2019. "A nonparametric importance sampling estimator for moment independent importance measures," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 3-16.
    12. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    13. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
    14. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.
    15. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    16. Han, Chaoshuai & Liu, Kun & Ma, Yongliang & Qin, Peijiang & Zou, Tao, 2021. "Multiaxial fatigue assessment of jacket-supported offshore wind turbines considering multiple random correlated loads," Renewable Energy, Elsevier, vol. 169(C), pages 1252-1264.
    17. Liu, Fuchao & Wei, Pengfei & Tang, Chenghu & Wang, Pan & Yue, Zhufeng, 2019. "Global sensitivity analysis for multivariate outputs based on multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 287-298.
    18. Terrero-Gonzalez, Alicia & Dai, Saishuai & Neilson, Richard D. & Papadopoulos, Jim & Kapitaniak, Marcin, 2024. "Dynamic response of a shallow-draft floating wind turbine concept: Experiments and modelling," Renewable Energy, Elsevier, vol. 226(C).
    19. Elmar Plischke & Emanuele Borgonovo, 2020. "Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions," Risk Analysis, John Wiley & Sons, vol. 40(12), pages 2639-2660, December.
    20. Manel Baucells & Emanuele Borgonovo, 2013. "Invariant Probabilistic Sensitivity Analysis," Management Science, INFORMS, vol. 59(11), pages 2536-2549, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:212:y:2023:i:c:p:570-588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.