Abstract
Gearbox in offshore wind turbines is a component with the highest failure rates during operation. Analysis of gearbox repair policy that includes economic considerations is important for the effective operation of offshore wind farms. From their initial perfect working states, gearboxes degrade with time, which leads to decreased working efficiency. Thus, offshore wind turbine gearboxes can be considered to be multi-state systems with the various levels of productivity for different working states. To efficiently compute the time-dependent distribution of this multi-state system and analyze its reliability, application of the non-homogeneous continuous-time Markov process (NHCTMP) is appropriate for this type of object. To determine the relationship between operation time and maintenance cost, many factors must be taken into account, including maintenance processes and vessel requirements. Finally, an optimal repair policy can be formulated based on this relationship.
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References
Carroll J, McDonald A, McMillan D (2015) Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines. Wind Energy 19:1107–1119. https://doi.org/10.1002/we.1887
Chan D, Mo J (2017) Life cycle reliability and maintenance analyses of wind turbines. 1st International Conference on Energy and Power. Melbourne, Australia. https://doi.org/10.1016/j.egypro.2017.03.148
Chehouri A, Younes R, Ilinca A, Perron J (2015) Review of performance optimization techniques applied to wind turbines. Appl Energy 142:361–388. https://doi.org/10.1016/j.apenergy.2014.12.043
Erguido A, Crespo Márquez A, Castellano E, Gómez Fernándezb JF (2017) A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms. Renew Energy 14:843–856. https://doi.org/10.1016/j.renene.2017.07.017
Feng Y, Tavner P, Long H (2010) Early experiences with UK Round I offshore wind farms. Proc Inst Civ Eng Energ 163(4):167–181. https://doi.org/10.1680/ener.2010.163.4.167
Hahn B, Durstewitz M, Rohrig K (2007) Reliability of wind turbines. Wind Energy 329–332. https://doi.org/10.1007/978-3-540-33866-6_62
Iscioglu F (2017) Dynamic performance evaluation of multi–state systems under non–homogeneous continuous time Markov process degradation using lifetimes in terms of order statistics. Journal of Risk and Reliability 231:255–264. https://doi.org/10.1177/1748006X17695710
Kang JC, Li MX, Sun LP, Wang M (2017) Preventative maintenance optimization for offshore wind turbine gearbox. Proceedings of the 27th International Ocean and Polar Engineering Conference
Le B, Andrew J (2016) Modelling wind turbine degradation and maintenance. Wind Energy 19:571–591. https://doi.org/10.1002/we.1851
Li MX, Kang JC, Sun LP, Wang M (2017) Reliability analysis of offshore wind turbine gearbox. Proceedings of the 6th international conference on marine structures. Lisbon, Portugal. https://doi.org/10.1201/9781315157368-104
Marquez FPG, Perez JMP, Marugan AP, Papaelias M (2015) Identification of critical components of wind turbines using FTA over the time. Renew Energy 87:869–883. https://doi.org/10.1016/j.renene.2015.09.038
Ossai CI, Boswell B, Davies I (2016) A Markovian approach for modelling the effects of maintenance on downtime and failure risk of wind turbine components. Renew Energy 96:775–783. https://doi.org/10.1016/j.renene.2016.05.022
Santos F, Teixieira AP, Guedes Soares C (2015) Modelling and simulation of the operation and maintenance of offshore wind turbines. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 229(5):385–393. https://doi.org/10.1177/1748006X15589209
Savino MM, Manzini R, Della Selva V, Accorsi R (2017) A new model for environmental and economic evaluation of renewable energy systems: the case of wind turbines. Appl Energy 189:739–752. https://doi.org/10.1016/j.apenergy.2016.11.124
Sedaghat A, Hassanzadeh A, Jamali J, Mostafaeipour A, Chen WH (2017) Determination of rated wind speed for maximum annual energy production of variable speed wind turbines. Appl Energy 205:781–789. https://doi.org/10.1016/j.apenergy.2017.08.079
Sheu SH, Chang CC, Chen YL, Zhang ZG (2015) Optimal preventive maintenance and repair policies for multi-state systems. Reliab Eng Syst Saf 140:78–87. https://doi.org/10.1016/j.ress.2015.03.029
Spinato F, Tavner PJ, Bussel GJW, Koutoulakos E (2008) Reliability of wind turbine subassemblies. IET Renew Power Gener 3(4):389–104. https://doi.org/10.1049/iet-rpg.2008.0060
Wu F, Niknam SA, Kobza JE (2015) A cost effective degradation-based maintenance strategy under imperfect repair. Reliab Eng Syst Saf 144:234–243. https://doi.org/10.1016/j.ress.2015.08.002
Xu J, Li L, Zheng B (2016) Wind energy generation technological paradigm diffusion. Renew Sust Energ Rev 59:436–449. https://doi.org/10.1016/j.rser.2015.12.271
Zountouridou E, Kiokes G, Chakalis S, Gerogilakis P, Hatziargyriou N (2015) Offshore floating wind parks in the deep waters of Mediterranean Sea. Renew Sust Energ Rev 51:433–448. https://doi.org/10.1016/j.rser.2015.06.027
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Article Highlights
• In order to decrease the operation and maintenance cost for the offshore wind farms, the maintenance policies for wind turbines must be optimized.
• The non-homogeneous continuous-time Markov process is applied on the reliability analysis of offshore wind turbines gearboxes.
• Considering the cost of each parts of the maintenance schedule, the maintenance policy formulated will be the most economical.
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Li, M., Kang, J., Sun, L. et al. Development of Optimal Maintenance Policies for Offshore Wind Turbine Gearboxes Based on the Non-homogeneous Continuous-Time Markov Process. J. Marine. Sci. Appl. 18, 93–98 (2019). https://doi.org/10.1007/s11804-019-00075-9
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DOI: https://doi.org/10.1007/s11804-019-00075-9