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An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics

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  • Zhou, P.
  • Yin, P.T.

Abstract

The reduction of operation and maintenance cost plays a significant role in decreasing the cost of energy generated by offshore wind farm, which can be realized through better design of maintenance strategy. This paper proposes a dynamic opportunistic condition-based maintenance strategy for offshore wind farm by using predictive analytics. In the strategy, a new maintenance basis is developed by considering the varying maintenance lead time to make maintenance decisions for different wind turbine components. Meanwhile, the strategy also considers the economic dependence between the wind turbines and the components. We then present a maintenance model to derive the optimal maintenance plans for various turbine components under different weather and operation load conditions. A numerical example is used to illustrate the effectiveness of the maintenance model. It is found that the varying maintenance lead time has a significant effect on the annual maintenance cost which demonstrates the reasonableness of our proposed maintenance basis. Compared to the widely employed and the simple maintenance strategies, the proposed strategy can help reduce the annual maintenance cost by 39.24% and 32.46% respectively.

Suggested Citation

  • Zhou, P. & Yin, P.T., 2019. "An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 1-9.
    • Handle: RePEc:eee:rensus:v:109:y:2019:i:c:p:1-9
    DOI: 10.1016/j.rser.2019.03.049
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    5. Jichuan Kang & Zihao Wang & C. Guedes Soares, 2020. "Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine," Energies, MDPI, vol. 13(14), pages 1-17, July.
    6. Bakir, I. & Yildirim, M. & Ursavas, E., 2021. "An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    7. Wang, Yifei & He, Rui & Tian, Zhigang, 2023. "Opportunistic condition-based maintenance optimization for electrical distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    8. Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    10. McMorland, J. & Collu, M. & McMillan, D. & Carroll, J. & Coraddu, A., 2023. "Opportunistic maintenance for offshore wind: A review and proposal of future framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    11. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2023. "A closed-loop maintenance strategy for offshore wind farms: Incorporating dynamic wind farm states and uncertainty-awareness in decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    12. Mario Vieira & Brian Snyder & Elsa Henriques & Craig White & Luis Reis, 2023. "Economic Viability of Implementing Structural Health Monitoring Systems on the Support Structures of Bottom-Fixed Offshore Wind," Energies, MDPI, vol. 16(13), pages 1-20, June.
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    14. Fallahi, F. & Bakir, I. & Yildirim, M. & Ye, Z., 2022. "A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Nguyen, Thi-Anh-Tuyet & Chou, Shuo-Yan & Yu, Tiffany Hui-Kuang, 2022. "Developing an exhaustive optimal maintenance schedule for offshore wind turbines based on risk-assessment, technical factors and cost-effective evaluation," Energy, Elsevier, vol. 249(C).

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