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Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms

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  • Zhou, Yifan
  • Miao, Jindan
  • Yan, Bin
  • Zhang, Zhisheng

Abstract

Maintenance scheduling (MS) for wind turbines (WTs) is an emerging investigation area in recent years. The MS of WTs is more complex than that of traditional thermal generators, because maintenance activities of WTs are affected by stochastic weather conditions, e.g., wind speed and precipitation. This paper proposes a long-term MS method to obtain the joint preventive maintenance plan during the whole warranty period of WTs on multiple wind farms. Both the labour cost and production loss are used as objective functions of the MS. Historical weather data are analysed, and a statistical model is developed to describe the weather conditions. Then, the MS problem is formulated compactly as a mixed integer linear programming model. Finally, a detailed practical case study is demonstrated to validate the effectiveness of the proposed MS method. The result confirms that cost-effective joint preventive maintenance (PM) plans of three wind farms can be derived through the proposed MS method. Compared with the periodic PM plan, the expected labour cost and production loss are reduced by approximately 30% and 20%, respectively.

Suggested Citation

  • Zhou, Yifan & Miao, Jindan & Yan, Bin & Zhang, Zhisheng, 2020. "Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms," Renewable Energy, Elsevier, vol. 160(C), pages 1136-1147.
    • Handle: RePEc:eee:renene:v:160:y:2020:i:c:p:1136-1147
    DOI: 10.1016/j.renene.2020.07.065
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    1. Pablo Ballesteros-Pérez & Stefán Thor Smith & Josephine Gwen Lloyd-Papworth & Peter Cooke, 2018. "Incorporating the effect of weather in construction scheduling and management with sine wave curves: application in the United Kingdom," Construction Management and Economics, Taylor & Francis Journals, vol. 36(12), pages 666-682, December.
    2. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    3. Li, Xuan & Zhang, Wei, 2020. "Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures," Renewable Energy, Elsevier, vol. 147(P1), pages 764-775.
    4. Perez-Canto, Salvador & Rubio-Romero, Juan Carlos, 2013. "A model for the preventive maintenance scheduling of power plants including wind farms," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 67-75.
    5. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    6. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
    7. Froger, Aurélien & Gendreau, Michel & Mendoza, Jorge E. & Pinson, Éric & Rousseau, Louis-Martin, 2016. "Maintenance scheduling in the electricity industry: A literature review," European Journal of Operational Research, Elsevier, vol. 251(3), pages 695-706.
    8. Peyman Mazidi & Yaser Tohidi & Miguel A. Sanz-Bobi, 2017. "Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System," Energies, MDPI, vol. 10(3), pages 1-20, March.
    9. Abdollahzadeh, Hadi & Atashgar, Karim & Abbasi, Morteza, 2016. "Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups," Renewable Energy, Elsevier, vol. 88(C), pages 247-261.
    10. Katinas, Vladislovas & Marčiukaitis, Mantas & Gecevičius, Giedrius & Markevičius, Antanas, 2017. "Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania," Renewable Energy, Elsevier, vol. 113(C), pages 190-201.
    11. Gutierrez-Alcoba, A. & Hendrix, E.M.T. & Ortega, G. & Halvorsen-Weare, E.E. & Haugland, D., 2019. "On offshore wind farm maintenance scheduling for decision support on vessel fleet composition," European Journal of Operational Research, Elsevier, vol. 279(1), pages 124-131.
    12. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    13. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    14. Nguyen, Thi Anh Tuyet & Chou, Shuo-Yan, 2019. "Improved maintenance optimization of offshore wind systems considering effects of government subsidies, lost production and discounted cost model," Energy, Elsevier, vol. 187(C).
    15. Stock-Williams, Clym & Swamy, Siddharth Krishna, 2019. "Automated daily maintenance planning for offshore wind farms," Renewable Energy, Elsevier, vol. 133(C), pages 1393-1403.
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