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Dynamic response analysis of floating wind turbine platform in local fatigue of mooring

Author

Listed:
  • Sun, Kang
  • Xu, Zifei
  • Li, Shujun
  • Jin, Jiangtao
  • Wang, Peilin
  • Yue, Minnan
  • Li, Chun

Abstract

The moorings of the Floating Wind Turbine (FWT) platforms, long term suffering from the coupling loads of wind, waves and currents, are especially prone to structural fatigue. The purpose of this study is to mine effective information from the dynamic response of the FWT platform to achieve early damage detection for mooring health conditions. However, high nonlinearity of the FWT platform dynamic response, that is caused by the complexity of the working environment, hinders the accuracy of fatigue analysis and damage detection, Therefore, in this study, motivated by the accuracy of the chaotic features in quantifying nonlinearities and reliability of the convolutional neural network for feature extraction, an intelligent damage detection model, named Convolutional Neural Network-t-distribution Stochastic Neighbor Embedding (CNN-t-SNE), is proposed to automatically detect the damage magnitude of the moorings. Through analyzing the dynamics of FWT platform mooring from structure creep to failure, it is found that the yaw response is the most sensitive to structural damage. To examine the reliability of the proposed CNN-t-SNE method, the Lyapunov exponent and chaotic attractor quantify the nonlinearity of the features in the neural networks to indicate that the nonlinearity of the features decreases as the neural network layer deepens.

Suggested Citation

  • Sun, Kang & Xu, Zifei & Li, Shujun & Jin, Jiangtao & Wang, Peilin & Yue, Minnan & Li, Chun, 2023. "Dynamic response analysis of floating wind turbine platform in local fatigue of mooring," Renewable Energy, Elsevier, vol. 204(C), pages 733-749.
    • Handle: RePEc:eee:renene:v:204:y:2023:i:c:p:733-749
    DOI: 10.1016/j.renene.2022.12.117
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    References listed on IDEAS

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    1. Li, Yan & Zhu, Qiang & Liu, Liqin & Tang, Yougang, 2018. "Transient response of a SPAR-type floating offshore wind turbine with fractured mooring lines," Renewable Energy, Elsevier, vol. 122(C), pages 576-588.
    2. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    3. Kong, Xiaobing & Ma, Lele & Wang, Ce & Guo, Shifan & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2022. "Large-scale wind farm control using distributed economic model predictive scheme," Renewable Energy, Elsevier, vol. 181(C), pages 581-591.
    4. Bae, Y.H. & Kim, M.H. & Kim, H.C., 2017. "Performance changes of a floating offshore wind turbine with broken mooring line," Renewable Energy, Elsevier, vol. 101(C), pages 364-375.
    5. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    6. Thanh-Dam Pham & Minh-Chau Dinh & Hak-Man Kim & Thai-Thanh Nguyen, 2021. "Simplified Floating Wind Turbine for Real-Time Simulation of Large-Scale Floating Offshore Wind Farms," Energies, MDPI, vol. 14(15), pages 1-18, July.
    7. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
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    Cited by:

    1. Zhanpu Xue & Hao Zhang & Yunguang Ji, 2023. "Dynamic Response of a Flexible Multi-Body in Large Wind Turbines: A Review," Sustainability, MDPI, vol. 15(8), pages 1-25, April.

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