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Structural health monitoring of tendons in a multibody floating offshore wind turbine under varying environmental and operating conditions

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  • Sakaris, Christos S.
  • Yang, Yang
  • Bashir, Musa
  • Michailides, Constantine
  • Wang, Jin
  • Sakellariou, John S.
  • Li, Chun

Abstract

The structural health monitoring of a Floating Offshore Wind Turbine (FOWT) tendons, taking into account the comprehensive damage diagnosis problem of damage detection, damaged tendon identification and damage precise quantification under varying environmental and operating conditions (EOCs), is investigated for the first time. The study examines a new concept of a 10 MW multibody FOWT whose tower is supported by a platform consisting of two rigid-body tanks connected by 12 tendons. Normal and the most severe EOCs from a site located in the northern coast of Scotland, are selected for the simulation of the FOWT structure under constant current but varying wind and wave conditions. Dynamic responses of the platform under different damage states are obtained based on the simulated FOWT. The damage scenarios are modelled via stiffness reduction (%) at the tendon's connection point to the platform's upper tank. Damage diagnosis is achieved via an advanced method, the Functional Model Based Method, that is formulated to operate using a single response signal and stochastic Functional Models representing the structural dynamics under the effects of varying EOCs and any magnitude of the considered damages. Due to the robustness and high number of the existing tendons, the effects of considered damages on the FOWT dynamics are minor and overlapped by the effects of the varying EOCs, indicating a highly challenging damage diagnosis problem. Very good damage detection results are obtained with the damage detection almost faultless and with no false alarms. Accurate damaged tendon identification is achieved for the 95% of the considered test cases, while the mean error in damage quantification is approximately equal to 4% using measurements from just a single accelerometer within a very limited frequency bandwidth of [0–5] Hz.

Suggested Citation

  • Sakaris, Christos S. & Yang, Yang & Bashir, Musa & Michailides, Constantine & Wang, Jin & Sakellariou, John S. & Li, Chun, 2021. "Structural health monitoring of tendons in a multibody floating offshore wind turbine under varying environmental and operating conditions," Renewable Energy, Elsevier, vol. 179(C), pages 1897-1914.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:1897-1914
    DOI: 10.1016/j.renene.2021.08.001
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    References listed on IDEAS

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    1. Wymore, Mathew L. & Van Dam, Jeremy E. & Ceylan, Halil & Qiao, Daji, 2015. "A survey of health monitoring systems for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 976-990.
    2. Yang, Yang & Bashir, Musa & Michailides, Constantine & Li, Chun & Wang, Jin, 2020. "Development and application of an aero-hydro-servo-elastic coupling framework for analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 161(C), pages 606-625.
    3. Yang, Yang & Bashir, Musa & Michailides, Constantine & Mei, Xuan & Wang, Jin & Li, Chun, 2021. "Coupled analysis of a 10 MW multi-body floating offshore wind turbine subjected to tendon failures," Renewable Energy, Elsevier, vol. 176(C), pages 89-105.
    4. Liu, Fushun & Li, Huajun & Li, Wei & Wang, Bin, 2014. "Experimental study of improved modal strain energy method for damage localisation in jacket-type offshore wind turbines," Renewable Energy, Elsevier, vol. 72(C), pages 174-181.
    5. Martinez-Luengo, Maria & Kolios, Athanasios & Wang, Lin, 2016. "Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 91-105.
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    3. Xie, Tianming & Xu, Qifa & Jiang, Cuixia & Lu, Shixiang & Wang, Xiangxiang, 2023. "The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 202(C), pages 143-153.

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