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An Optimal Number-Dependent Preventive Maintenance Strategy for Offshore Wind Turbine Blades Considering Logistics

DOI: 10.1155/2013/205847

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In offshore wind turbines, the blades are among the most critical and expensive components that suffer from different types of damage due to the harsh maritime environment and high load. The blade damages can be categorized into two types: the minor damage, which only causes a loss in wind capture without resulting in any turbine stoppage, and the major (catastrophic) damage, which stops the wind turbine and can only be corrected by replacement. In this paper, we propose an optimal number-dependent preventive maintenance (NDPM) strategy, in which a maintenance team is transported with an ordinary or expedited lead time to the offshore platform at the occurrence of the th minor damage or the first major damage, whichever comes first. The long-run expected cost of the maintenance strategy is derived, and the necessary conditions for an optimal solution are obtained. Finally, the proposed model is tested on real data collected from an offshore wind farm database. Also, a sensitivity analysis is conducted in order to evaluate the effect of changes in the model parameters on the optimal solution. 1. Introduction Wind energy has become an attractive source of renewable energy in the European energy market because it is free, abundant, and perceived as having a low impact on the environment. Over the past five years (2008–2012), the wind energy industry has been the fastest growing renewable energy source with an annual average growth rate of 28% [1]. For instance, in Sweden, 763?MW of wind power (onshore and offshore) was installed in 2011 which increased the wind power capacity to 2906 MW—about 2% of the total electricity consumption [2]. Certain forecasts indicate that the share of wind power in Sweden’s electricity generation will reach up to 20% by 2020. Nowadays more and more wind turbines are being installed offshore due to the high potential of wind energy, less visual disturbance, and larger potential areas for installation. Presently, there are five offshore wind farms in the sea waters of Sweden (Lillgrund, Vanem, Utgrunden, Yttre Stengrund, and Bockstigen) with a total operating capacity of 163.7?MW [3]. However, a wind power system located at sea comes with higher installation costs and more difficult maintenance conditions compared to an onshore system. Furthermore, an offshore wind turbine has undesirable features like a higher failure rate, lower reliability, and higher operation and maintenance (O&M) costs. The O&M costs of onshore wind turbines account for around 20–25% of the wind energy generation cost, whereas in offshore wind farms, they

References

[1]  European Wind Energy Association, Wind in power, 2012 European statistics, 2013, http://www.ewea.org/fileadmin/files/library/publications/statistics/Wind_in_power_annual_statistics_2012.pdf.
[2]  Swedish Energy Agency, Energy in Sweden 2011, 2012, http://www.energimyndigheten.se/en/About-us/Press-/News/New-publication-Energy-in-Sweden-2011/.
[3]  M. Bilgili, A. Yasar, and E. Simsek, “Offshore wind power development in Europe and its comparison with onshore counterpart,” Renewable and Sustainable Energy Reviews, vol. 15, no. 2, pp. 905–915, 2011.
[4]  M. Nordahl, The development of a life cycle cost model for an offshore wind farm [M.S. thesis], Department of Applied Mechanics, Chalmers University of Technology, 2011, http://publications.lib.chalmers.se/records/fulltext/152402.pdf.
[5]  E. Byon, L. Ntaimo, and Y. Ding, “Optimal maintenance strategies for wind turbine systems under stochastic weather conditions,” IEEE Transactions on Reliability, vol. 59, no. 2, pp. 393–404, 2010.
[6]  E. Byon and Y. Ding, “Season-dependent condition-based maintenance for a wind turbine using a partially observed markov decision process,” IEEE Transactions on Power Systems, vol. 25, no. 4, pp. 1823–1834, 2010.
[7]  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.
[8]  J. J. Nielsen and J. D. S?rensen, “On risk-based operation and maintenance of offshore wind turbine components,” Reliability Engineering and System Safety, vol. 96, no. 1, pp. 218–229, 2011.
[9]  F. Ding and Z. Tian, “Opportunistic maintenance optimization for wind turbine systems considering imperfect maintenance actions,” International Journal of Reliability, Quality and Safety Engineering, vol. 18, no. 5, pp. 463–482, 2011.
[10]  F. Ding and Z. Tian, “Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds,” Renewable Energy, vol. 45, pp. 175–182, 2012.
[11]  L. W. M. M. Radmakers, H. Braam, M. B. Zaaijer, and G. J. W. van Bussel, Assessment and optimization of operation and maintenance of offshore wind turbines, 2003, http://www.ecn.nl/fileadmin/ecn/units/wind/docs/dowec/2003-EWEC-O_M.pdf.
[12]  G. M. J. Herbert, S. Iniyan, and R. Goic, “Performance, reliability and failure analysis of wind farm in a developing Country,” Renewable Energy, vol. 35, no. 12, pp. 2739–2751, 2010.
[13]  D. J. Lekou and P. Vionis, “Report on repair techniques for composite parts of wind turbine blades,” Tech. Rep., 2002, http://www.wmc.eu/public_docs/10059_000.pdf.
[14]  A. Saeed, Online condition monitoring system for wind turbine: case study [M.S. thesis], Department of Electrical Engineering, Blekinge Institute of Technology, 2008, http://www.bth.se/fou/cuppsats.nsf/all/3a7cde7b4a7d576ec12578160076cf54/$file/Wind_Turbine-Master_thesis-BTH.pdf.
[15]  H. Makabe and H. Morimura, “A new policy for preventive maintenance,” Journal of Operations Research Society of Japan, vol. 5, pp. 110–124, 1963.
[16]  H. Makabe and H. Morimura, “On some preventive maintenance policies,” Journal of Operations Research Society of Japan, vol. 6, pp. 17–47, 1963.
[17]  H. Makabe and H. Morimura, “Some considerations on preventive maintenance policies with numerical analysis,” Journal of the Operations Research Society of Japan, vol. 7, pp. 154–171, 1965.
[18]  H. Morimura, “On some preventive maintenance policies for IFR,” Journal of the Operations Research Society of Japan, vol. 12, pp. 94–124, 1970.
[19]  K. S. Park, “Optimal number of minimal repairs before replacement,” IEEE Transactions on Reliability, vol. 28, no. 2, pp. 137–140, 1979.
[20]  T. Nakagawa, “Generalized models for determining optimal number of minimal repairs before replacement,” Journal of the Operations Research Society of Japan, vol. 24, no. 4, pp. 325–337, 1981.
[21]  S. H. Sheu and W. S. Griffith, “Optimal number of minimal repairs before replacement of a system subject to shocks,” Naval Research Logistics, vol. 43, no. 3, pp. 319–333, 1996.
[22]  S. H. Sheu, “A generalized model for determining optimal number of minimal repairs before replacement,” European Journal of Operational Research, vol. 69, no. 1, pp. 38–49, 1993.
[23]  S. H. Sheu, W. S. Griffith, and T. Nakagawa, “Extended optimal replacement model with random minimal repair costs,” European Journal of Operational Research, vol. 85, no. 3, pp. 636–649, 1995.
[24]  K. S. Park, “Optimal number of minor failures before replacement,” International Journal of Systems Science, vol. 18, no. 2, pp. 333–337, 1987.
[25]  C. C. Chang, S. H. Sheu, and Y. L. Chen, “Optimal number of minimal repairs before replacement based on a cumulative repair-cost limit policy,” Computers & Industrial Engineering, vol. 59, no. 4, pp. 603–610, 2010.
[26]  S. H. Sheu, C. C. Chang, and Y. H. Chien, “Optimal age-replacement time with minimal repair based on cumulative repair-cost limit for a system subject to shocks,” Annals of Operations Research, vol. 186, pp. 317–329, 2011.
[27]  S. H. Sheu and Y. H. Chien, “Optimal age-replacement policy of a system subject to shocks with random lead-time,” European Journal of Operational Research, vol. 159, no. 1, pp. 132–144, 2004.
[28]  Y. H. Chien, “A number-dependent replacement policy for a system with continuous preventive maintenance and random lead times,” Applied Mathematical Modelling, vol. 33, no. 3, pp. 1708–1718, 2009.
[29]  Y. H. Chien, “Optimal number of minimal repairs before ordering spare for preventive replacement,” Applied Mathematical Modelling, vol. 34, no. 11, pp. 3439–3450, 2010.
[30]  T. Nakagawa, Shock and Damage Models in Reliability Theory, Springer, London, UK, 2007.
[31]  E. Byon, L. Ntaimo, C. Singh, and Y. Ding, “Wind energy facility reliability and maintenance,” in Handbook of Wind Power Systems: Optimization, Modeling, Simulation and Economic Aspects, P. Pardalos, M. V. Pereira, S. Rebennack, et al., Eds., Springer, London, UK, 2013.
[32]  J. H. Bookbinder and M. ?akanyildirim, “Random lead times and expedited orders in (Q, r) inventory systems,” European Journal of Operational Research, vol. 115, no. 2, pp. 300–313, 1999.
[33]  J. A. Andrawus, Maintenance optimization for wind turbines [Ph.D. thesis], Robert Gordon University, 2008, https://openair.rgu.ac.uk/handle/10059/268.
[34]  S. M. Ross, Applied Probability Models with Optimization Applications, Holden-Day, San Francisco, Calif, USA, 1970.
[35]  F. Besnard and L. Bertling, “An approach for condition-based maintenance optimization applied to wind turbine blades,” IEEE Transactions on Sustainable Energy, vol. 1, no. 2, pp. 77–83, 2010.
[36]  A. Karyotakis, On the optimization of operation and maintenance strategies for offshore wind farms [Ph.D. thesis], Department of Mechanical Engineering, University College London, 2011, http://www.ecn.nl/docs/library/report/2011/m11103.pdf.

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