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Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions

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  • Li, Xuan
  • Zhang, Wei

Abstract

Offshore wind energy has gained widespread attention and experienced a rapid development due to the significantly increasing demand for renewable energy over the past few years. Currently, the development of offshore floating wind turbines attracts lots of attention to harvest more energy from a sustained higher speed of offshore wind away from the coastline. With stronger cyclic wind and wave loadings, the floating wind turbine could possibly experience severe fatigue damages at certain critical locations, which might lead to a catastrophic failure. Evaluating accumulated fatigue damage for a floating wind turbine during its entire lifetime, therefore, becomes essential and urgent. As demonstrated in the codes, specifications, or design practices, fatigue assessments require massive computational costs and pose challenges to numerical simulations since lots of dynamic analyses under different environmental scenarios need to be performed. To reduce the calculation cost for this time-consuming process while maintaining high accuracy, a probabilistic long-term fatigue damage assessment approach is proposed in the present study by implementing a C-vine copula model and a surrogate model. The C-vine copula model provides a multivariate dependency description for the on-site wind and wave-related environmental parameters. Two surrogate models, including the Kriging model and the artificial neural network (ANN), are implemented to efficiently predict the short-term fatigue damages at critical locations of the floating wind turbine. The proposed long-term fatigue damage assessment framework is accurate and suitable for evaluating structural long-term fatigue damages accumulated in a real environment especially when effects from more environmental parameters are to be considered. Based on surrogate models, sensitivity analyses are carried out to investigate the relative significance of each environmental parameter on short-term fatigue damages. In addition, uncertainties from short-term fatigue damages are also incorporated into the probabilistic fatigue evaluation framework to assess the accumulated long-term fatigue damages for a spar type floating wind turbine.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:159:y:2020:i:c:p:570-584
    DOI: 10.1016/j.renene.2020.06.043
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Zhu, Dongping & Huang, Xiaogang & Ding, Zhixia & Zhang, Wei, 2024. "Estimation of wind turbine responses with attention-based neural network incorporating environmental uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Bowen Zhou & Zhibo Zhang & Guangdi Li & Dongsheng Yang & Matilde Santos, 2023. "Review of Key Technologies for Offshore Floating Wind Power Generation," Energies, MDPI, vol. 16(2), pages 1-26, January.
    4. 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.
    5. 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.
    6. 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).
    7. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    8. 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.
    9. 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.
    10. 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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