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Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures

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  • Long, Lijia
  • Mai, Quang Anh
  • Morato, Pablo Gabriel
  • Sørensen, John Dalsgaard
  • Thöns, Sebastian

Abstract

The use of load and structural performance measurement information is vital for efficient structural integrity management and for the cost of energy production with Offshore Wind Turbines (OWTs). OWTs are dynamically sensitive structures subject to an interaction with a control unit exposed to repeated cyclic wind and wave loads causing deterioration and fatigue. This study focuses on the quantification of the value of structural and environmental information on the integrity management of OWT structures, with the focus on fatigue of welded joints. By utilizing decision analysis, structural reliability methods, measurement data, as well as the cost-benefit models, a Value of Information (VoI) analysis can be performed to quantify the most beneficial measurement strategy. The VoI assessment is demonstrated for the integrity management of a butt welded joint of a monopile support structure for a 3 MW OWT with a hub height of approximately 71m. The conditional value of three-year measured oceanographic information and one-year strain monitoring information is quantified posteriori in conjunction with an inspection and repair planning. This paper provides insights on how much benefits can be achieved through structural and environmental information, with practical relevance on reliability-based maintenance of OWT structures.

Suggested Citation

  • Long, Lijia & Mai, Quang Anh & Morato, Pablo Gabriel & Sørensen, John Dalsgaard & Thöns, Sebastian, 2020. "Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures," Renewable Energy, Elsevier, vol. 159(C), pages 1036-1046.
    • Handle: RePEc:eee:renene:v:159:y:2020:i:c:p:1036-1046
    DOI: 10.1016/j.renene.2020.06.038
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    References listed on IDEAS

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    1. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
    2. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    3. Maria Martinez-Luengo & Mahmood Shafiee, 2019. "Guidelines and Cost-Benefit Analysis of the Structural Health Monitoring Implementation in Offshore Wind Turbine Support Structures," Energies, MDPI, vol. 12(6), pages 1-26, March.
    4. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    5. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
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    1. Morato, P.G. & Andriotis, C.P. & Papakonstantinou, K.G. & Rigo, P., 2023. "Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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