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All issues Volume 233 (2021) E3S Web Conf., 233 (2021) 01063 References
Open Access
Issue
E3S Web Conf.
Volume 233, 2021
2020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
Article Number 01063
Number of page(s) 7
Section NESEE2020-New Energy Science and Environmental Engineering
DOI https://doi.org/10.1051/e3sconf/202123301063
Published online 27 January 2021
  1. G. Van Bussel, “State of the art and technology trends for offshore wind energy: operation and maintenance issues,” in Offshore Wind Energy EWEA special topic conference, 2001. [Google Scholar]
  2. G. Van Bussel and M. Zaaijer, “Reliability, availability and maintenance aspects of large-scale offshore wind farms, a concepts study,” in Proceedings of MAREC, 2001, vol. 2001. [Google Scholar]
  3. E. E. Halvorsen-Weare, C. Gundegjerde, I. B. Halvorsen, L. M. Hvattum, and L. M. Nonås, “Vessel Fleet Analysis for Maintenance Operations at Offshore Wind Farms,” Energy Procedia, vol. 35, pp. 167-176, 2013, doi: 10.1016/j.egypro.2013.07.170. [Google Scholar]
  4. Y. Sinha and J. A. Steel, “A progressive study into offshore wind farm maintenance optimisation using risk based failure analysis,” Renewable and Sustainable Energy Reviews, vol. 42, pp. 735-742, 2015. [CrossRef] [Google Scholar]
  5. K. Ortegon, L. F. Nies, and J. W. Sutherland, “Preparing for end of service life of wind turbines,” Journal of Cleaner Production, vol. 39, pp. 191-199, 2013. [Google Scholar]
  6. M. Shafiee, F. Brennan, and I. A. Espinosa, “A parametric whole life cost model for offshore wind farms,” The International Journal of Life Cycle Assessment, vol. 21, no. 7, pp. 961-975, 2016. [Google Scholar]
  7. O. Anaya-Lara, J. O. Tande, K. Uhlen, and K. Merz, Offshore Wind Energy Technology. John Wiley & Sons, 2018. [CrossRef] [Google Scholar]
  8. M. Hofmann, “A review of decision support models for offshore wind farms with an emphasis on operation and maintenance strategies,” Wind Engineering, vol. 35, no. 1, pp. 1-15, 2011. [CrossRef] [Google Scholar]
  9. Z. Tian, T. Jin, B. Wu, and F. Ding, “Condition based maintenance optimization for wind power generation systems under continuous monitoring,” Renewable Energy, vol. 36, no. 5, pp. 1502-1509, 2011, doi: 10.1016/j.renene.2010.10.028. [Google Scholar]
  10. M. Shafiee, “Maintenance logistics organization for offshore wind energy: Current progress and future perspectives,” Renewable Energy, vol. 77, pp. 182-193, 2015, doi: 10.1016/j.renene.2014.11.045. [Google Scholar]
  11. A. Sharma, G. Yadava, and S. Deshmukh, “A literature review and future perspectives on maintenance optimization,” Journal of Quality in Maintenance Engineering, vol. 17, no. 1, pp. 5-25, 2011. [Google Scholar]
  12. J. D. Sørensen, “Framework for risk-based planning of operation and maintenance for offshore wind turbines,” Wind energy, vol. 12, no. 5, pp. 493-506, 2009. [CrossRef] [Google Scholar]
  13. F. Ding, Z. Tian, and T. Jin, “Maintenance modeling and optimization for wind turbine systems: A review,” in Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on, 2013: IEEE, pp. 569-575. [CrossRef] [Google Scholar]
  14. M. Shafiee and J. D. Sørensen, “Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies,” Reliability Engineering & System Safety, 2017, doi: 10.1016/j.ress.2017.10.025. [Google Scholar]
  15. J. Fakcharoenphol, C. Harrelson, and S. Rao, “The k-traveling repairman problem,” in Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms, 2003: Society for Industrial and Applied Mathematics, pp. 655-664. [Google Scholar]
  16. F. Afrati, S. Cosmadakis, C. H. Papadimitriou, G. Papageorgiou, and N. Papakostantinou, “The complexity of the travelling repairman problem,” RAIRO-Theoretical Informatics and Applications, vol. 20, no. 1, pp. 79-87, 1986. [CrossRef] [EDP Sciences] [Google Scholar]
  17. S. Irani, X. Lu, and A. Regan, “On-line algorithms for the dynamic traveling repair problem,” Journal of Scheduling, vol. 7, no. 3, p. 243, 2004. [CrossRef] [Google Scholar]
  18. F. Camci, “Maintenance scheduling of geographically distributed assets with prognostics information,” European Journal of Operational Research, vol. 245, no. 2, pp. 506-516, 2015, doi: 10.1016/j.ejor.2015.03.023. [Google Scholar]
  19. L. Rademakers, H. Braam, T. Obdam, P. Frohböse, and N. Kruse, “Tools for estimating operation and maintenance costs of offshore wind farms: State of the Art,” in Proc. of EWEC, 2008. [Google Scholar]
  20. L. Rademakers, H. Braam, T. Obdam, and R. vd Pieterman, “Operation and maintenance cost estimator (OMCE) to estimate the future O&M costs of offshore wind farms,” in European Offshore Wind 2009 Conference, 2009, vol. 1, pp. 14-16. [Google Scholar]
  21. L. Dai, M. Stålhane, and I. B. Utne, “Routing and scheduling of maintenance fleet for offshore wind farms,” Wind Engineering, vol. 39, no. 1, pp. 15-30, 2015. [CrossRef] [Google Scholar]
  22. B. R. Sarker and T. I. Faiz, “Minimizing maintenance cost for offshore wind turbines following multi-level opportunistic preventive strategy,” Renewable Energy, vol. 85, pp. 104-113, 2016, doi: 10.1016/j.renene.2015.06.030. [Google Scholar]
  23. L. Li, M. You, and J. Ni, “Reliability-based dynamic maintenance threshold for failure prevention of continuously monitored degrading systems,” Journal of manufacturing science and engineering, vol. 131, no. 3, p. 031010, 2009. [Google Scholar]
  24. F. Camci, “The travelling maintainer problem: integration of condition-based maintenance with the travelling salesman problem,” Journal of the Operational Research Society, vol. 65, no. 9, pp. 1423-1436, 2017, doi: 10.1057/jors.2013.88. [CrossRef] [Google Scholar]
  25. M. Stålhane, L. M. Hvattum, and V. Skaar, “Optimization of Routing and Scheduling of Vessels to Perform Maintenance at Offshore Wind Farms,” Energy Procedia, vol. 80, pp. 92-99, 2015, doi: 10.1016/j.egypro.2015.11.411. [Google Scholar]
  26. A. Kovacs, G. Erdős, Z. J. Viharos, and L. Monostori, “A system for the detailed scheduling of wind farm maintenance,” CIRP Annals-Manufacturing Technology, vol. 60, no. 1, pp. 497-501, 2011. [CrossRef] [Google Scholar]
  27. A. F. Gutierrez, L. Dieulle, N. Labadie, and N. Velasco, “Wind farm maintenance scheduling model and solution approach.” [Google Scholar]
  28. C. A. Irawan, D. Ouelhadj, D. Jones, M. Stålhane, and I. B. Sperstad, “Optimisation of maintenance routing and scheduling for offshore wind farms,” European Journal of Operational Research, vol. 256, no. 1, pp. 76-89, 2017, doi: 10.1016/j.ejor.2016.05.059. [Google Scholar]
  29. A. Erguido, A. C. Marquez, E. Castellano, and J. F. G. Fernandez, “A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms,” (in English), Renewable Energy, vol. 114, pp. 843-856, Dec 2017, doi: 10.1016/j.renene.2017.07.017. [Google Scholar]
  30. A. Kovács, G. Erdös, L. Monostori, and Z. J. Viharos, “Scheduling the maintenance of wind farms for minimizing production loss,” IFAC Proceedings Volumes, vol. 44, no. 1, pp. 14802-14807, 2011. [CrossRef] [Google Scholar]
  31. M. Yildirim, N. Z. Gebraeel, and X. A. Sun, “Integrated Predictive Analytics and Optimization for Opportunistic Maintenance and Operations in Wind Farms,” (in English), Ieee Transactions on Power Systems, vol. 32, no. 6, pp. 4319-4328, Nov 2017, doi: 10.1109/Tpwrs.2017.2666722. [CrossRef] [Google Scholar]
  32. Z. Hameed, Y. Hong, Y. Cho, S. Ahn, and C. Song, “Condition monitoring and fault detection of wind turbines and related algorithms: A review,” Renewable and Sustainable energy reviews, vol. 13, no. 1, pp. 1-39, 2009. [CrossRef] [EDP Sciences] [Google Scholar]
  33. W. Zhao, D. Siegel, J. Lee, and L. Su, “An integrated framework of drivetrain degradation assessment and fault localization for offshore wind turbines,” IJPHM Special Issue on Wind Turbine PHM, vol. 4, p. 46, 2013. [Google Scholar]
  34. X. Jia, C. Jin, M. Buzza, Y. Di, D. Siegel, and J. Lee, “A deviation based assessment methodology for multiple machine health patterns classification and fault detection,” Mechanical Systems and Signal Processing, vol. 99, pp. 244-261, 2018. [Google Scholar]

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