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Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures

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  • Li, Xuan
  • Zhang, Wei

Abstract

Floating offshore wind turbines (FOWT) have been increasingly deployed to harvest the sustained high-speed offshore wind in recent years. Under the complex offshore environments, FOWT could experience significantly different environmental conditions with various occurrence frequencies during their lifetimes. In the present study, the dependence structure of six wind and wave-related environmental parameters (wind direction, wind speed, mean wave direction, significant wave height, peak spectral wave period, and wave directional spread) is established using a C-vine (canonical vine) copula model. Different families of bivariate copula are used as the building blocks to model the interdependence for each pair of environmental parameters. Thereafter, the environmental contour method and Rosenblatt transformation, according to a prescribed 50-year return period, are used to determine the long-term design loads at critical locations of a spar-type FOWT. Fractile level and multiplication factor are obtained for five different structural responses to provide guidance in parameter selections for future designs.

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  • 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.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:764-775
    DOI: 10.1016/j.renene.2019.09.076
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. 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).
    4. Zhou, Yifan & Miao, Jindan & Yan, Bin & Zhang, Zhisheng, 2020. "Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms," Renewable Energy, Elsevier, vol. 160(C), pages 1136-1147.
    5. 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.
    6. Ramadhani, Adhitya & Khan, Faisal & Colbourne, Bruce & Ahmed, Salim & Taleb-Berrouane, Mohammed, 2022. "Resilience assessment of offshore structures subjected to ice load considering complex dependencies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Xing Zheng Wu & Chen Zhe Ma & Rui-kai Wang & Wei Chao Li, 2023. "Development of environmental contours from rainfall intensity and duration data for slopes," 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. 116(1), pages 1001-1027, March.
    8. 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.
    9. 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.
    10. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    11. 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.
    12. 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.
    13. 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).

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