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A decision support system for strategic maintenance planning in offshore wind farms

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  • Li, Xiaodong
  • Ouelhadj, Djamila
  • Song, Xiang
  • Jones, Dylan
  • Wall, Graham
  • Howell, Kerry E.
  • Igwe, Paul
  • Martin, Simon
  • Song, Dongping
  • Pertin, Emmanuel

Abstract

This paper presents a Decision Support System (DSS) for maintenance cost optimisation at an Offshore Wind Farm (OWF). The DSS is designed for use by multiple stakeholders in the OWF sector with the overall goal of informing maintenance strategy and hence reducing overall lifecycle maintenance costs at the OWF. Two optimisation models underpin the DSS. The first is a deterministic model that is intended for use by stakeholders with access to accurate failure rate data. The second is a stochastic model that is intended for use by stakeholders who have less certainty about failure rates. Solutions of both models are presented using a UK OWF that is in construction as an example. Conclusions as to the value of failure rate data are drawn by comparing the results of the two models. Sensitivity analysis is undertaken with respect to the turbine failure rate frequency and number of turbines at the site, with near linear trends observed for both factors. Finally, overall conclusions are drawn in the context of maintenance planning in the OWF sector.

Suggested Citation

  • Li, Xiaodong & Ouelhadj, Djamila & Song, Xiang & Jones, Dylan & Wall, Graham & Howell, Kerry E. & Igwe, Paul & Martin, Simon & Song, Dongping & Pertin, Emmanuel, 2016. "A decision support system for strategic maintenance planning in offshore wind farms," Renewable Energy, Elsevier, vol. 99(C), pages 784-799.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:784-799
    DOI: 10.1016/j.renene.2016.07.037
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    Cited by:

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    2. Zhang, Chen & Gao, Wei & Yang, Tao & Guo, Sheng, 2019. "Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management," Renewable Energy, Elsevier, vol. 133(C), pages 703-711.
    3. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Shuo-Yan Chou & Xuan Loc Pham & Thi Anh Tuyet Nguyen & Tiffany Hui-Kuang Yu, 2023. "Optimal maintenance planning with special emphasis on deterioration process and vessel routing for offshore wind systems," Energy & Environment, , vol. 34(4), pages 739-763, June.
    5. Scarf, P.A. & Cavalcante, C.A.V. & Lopes, R.S., 2019. "Delay-time modelling of a critical system subject to random inspections," European Journal of Operational Research, Elsevier, vol. 278(3), pages 772-782.
    6. Santos, Marllen & González, Mario, 2019. "Factors that influence the performance of wind farms," Renewable Energy, Elsevier, vol. 135(C), pages 643-651.
    7. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    8. Xiaodong Li & Xiang Song & Djamila Ouelhadj, 2023. "A Cost Optimisation Model for Maintenance Planning in Offshore Wind Farms with Wind Speed Dependent Failure Rates," Mathematics, MDPI, vol. 11(13), pages 1-21, June.

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