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A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Author

Listed:
  • Harriet Fox

    (Industrial CDT in Offshore Renewable Energy, School of Engineering, The University of Edinburgh, Edinburgh EH8 9YL, UK
    EDF Energy R&D UK Centre, Croydon, London SE25 5AH, UK)

  • Ajit C. Pillai

    (College of Engineering, Mathematics and Physical Sciences, Exeter University, Penryn TR10 9FE, UK)

  • Daniel Friedrich

    (Industrial CDT in Offshore Renewable Energy, School of Engineering, The University of Edinburgh, Edinburgh EH8 9YL, UK)

  • Maurizio Collu

    (Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G1 1XQ, UK)

  • Tariq Dawood

    (EDF Energy R&D UK Centre, Croydon, London SE25 5AH, UK)

  • Lars Johanning

    (College of Engineering, Mathematics and Physical Sciences, Exeter University, Penryn TR10 9FE, UK
    College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China)

Abstract

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.

Suggested Citation

  • Harriet Fox & Ajit C. Pillai & Daniel Friedrich & Maurizio Collu & Tariq Dawood & Lars Johanning, 2022. "A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance," Energies, MDPI, vol. 15(2), pages 1-27, January.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:504-:d:722292
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    Citations

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    Cited by:

    1. Ugochukwu Ejike Akpudo & Jang-Wook Hur, 2022. "Investigating the Efficiencies of Fusion Algorithms for Accurate Equipment Monitoring and Prognostics," Energies, MDPI, vol. 15(6), pages 1-18, March.
    2. 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).
    3. Hamid Mirshekali & Athila Q. Santos & Hamid Reza Shaker, 2023. "A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids," Energies, MDPI, vol. 16(17), pages 1-29, August.
    4. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    5. 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).

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