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Guidelines and Cost-Benefit Analysis of the Structural Health Monitoring Implementation in Offshore Wind Turbine Support Structures

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  • Maria Martinez-Luengo

    (Centre for Renewable Energy Systems, Department of Energy and Power, Cranfield University, Bedfordshire MK43 0AL, UK)

  • Mahmood Shafiee

    (Centre for Renewable Energy Systems, Department of Energy and Power, Cranfield University, Bedfordshire MK43 0AL, UK)

Abstract

This paper investigates how the implementation of Structural Health Monitoring Systems (SHMS) in the support structure (SS) of offshore wind turbines (OWT) affects capital expenditure (CAPEX) and operational expenditure (OPEX) of offshore wind farms (WF). In order to determine the added value of Structural Health Monitoring (SHM), the balance between the reduction in OPEX and the increase in CAPEX is evaluated. In this paper, guidelines for SHM implementation in offshore WF are developed and applied to a baseline scenario. The application of these guidelines consist of a review of present regulations in the United Kingdom and Germany, the development of SHM strategy, where the first stage of the Statistical Pattern Recognition (SPR) paradigm is explored, failure modes that can be monitored are identified, and SHM technologies and sensor distributions within the turbines are described for a baseline scenario. Furthermore, an inspection strategy where the different structural inspections to be carried out above and below water is also developed, together with an inspection plan for the lifetime of the structures, for the aforementioned baseline scenario. Once the guidelines have been followed and the SHM and inspection strategies developed, a cost-benefit analysis is performed on the baseline case (10% instrumented assets) and three other scenarios with 20%, 30% and 50% of instrumented assets. Finally, a sensitivity analysis is conducted to evaluate the effects of SHM hardware cost and the time spent in completing the inspections on OPEX and CAPEX of the WF. The results show that SHM hardware cost increases CAPEX significantly, however this increase is much lower than the reduction in OPEX caused by SHM. The results also show that an increase in the percentage of instrumented assets will reduce OPEX and this reduction is considerably higher than the cost of SHM implementation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1176-:d:217341
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    References listed on IDEAS

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    1. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2018. "A lifecycle techno-economic model of offshore wind energy for different entry and exit instances," Applied Energy, Elsevier, vol. 221(C), pages 406-424.
    2. Maria Martinez Luengo & Athanasios Kolios, 2015. "Failure Mode Identification and End of Life Scenarios of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 8(8), pages 1-16, August.
    3. 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.
    4. Adedipe, Oyewole & Brennan, Feargal & Kolios, Athanasios, 2016. "Review of corrosion fatigue in offshore structures: Present status and challenges in the offshore wind sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 141-154.
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    Cited by:

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    2. de N Santos, Francisco & D’Antuono, Pietro & Robbelein, Koen & Noppe, Nymfa & Weijtjens, Wout & Devriendt, Christof, 2023. "Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks," Renewable Energy, Elsevier, vol. 205(C), pages 461-474.
    3. Mark Richmond & Ursula Smolka & Athanasios Kolios, 2020. "Feasibility for Damage Identification in Offshore Wind Jacket Structures through Monitoring of Global Structural Dynamics," Energies, MDPI, vol. 13(21), pages 1-24, November.
    4. 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.
    5. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    6. Wang, L. & Kolios, A. & Liu, X. & Venetsanos, D. & Rui, C., 2022. "Reliability of offshore wind turbine support structures: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Joseph Baquerizo & Christian Tutivén & Bryan Puruncajas & Yolanda Vidal & José Sampietro, 2022. "Siamese Neural Networks for Damage Detection and Diagnosis of Jacket-Type Offshore Wind Turbine Platforms," Mathematics, MDPI, vol. 10(7), pages 1-20, April.

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