Abstract
Wind energy is an important source of renewable energy, and reliability is a critical issue for operating wind energy systems. The Canadian wind energy industry has been growing very rapidly. The installed wind energy capacity in Canada in 2008 was approximately 2,000 mega watts (MW), which is less than one percent of the total electricity. It is believed that wind energy will satisfy 20% of Canada’s electricity demand by 2025, by adding 55,000MW of new generating capacity [1]. Operation and maintenance costs account for 25-30% of the wind energy generation cost. Currently, the wind turbine manufacturers and operators are gradually changing the maintenance strategy from time-based preventive maintenance to condition based maintenance (CBM) [2-5]. In this article, we review the current research status of maintenance of wind turbine systems, and discuss the applications of artificial neural networks (ANN) based health prediction tools in this field. A CBM method based on ANN health condition prediction is presented.
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References
Hau, E.: Wind turbines: fundamentals, technologies, application, economics. Springer, Heidelberg (2006)
Tavner, P.J., Xiang, J., Spinato, F.: Reliability analysis for wind turbines. Wind Energy 10, 1–18 (2007)
Krokoszinski, H.J.: Efficiency and effectiveness of wind farms - keys to cost optimized operation and maintenance. Renewable Energy 28(14), 2165–2178 (2003)
Sen, Z.: Statistical investigation of wind energy reliability and its application. Renewable Energy 10(1), 71–79 (1997)
Martinez, E., Sanz, F., Pellegrini, S.: Life cycle assessment of a multi-megawatt wind turbine. Renewable Energy 34(3), 667–673 (2009)
Jardine, A.K.S., Tsang, A.H.: Maintenance, Replacement, and Reliability Theory and Applications. CRC Press, Taylor & Francis Group (2006)
Sterzinger, G., Svrcek, M.: Wind turbine development: location of manufacturing activity. Renewable Energy Policy Project Technical Report (2004)
Tian, Z., Jin, T., Wu, B., Ding, F.: Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renewable Energy (2010) (accepted)
Andrawus, J.A.: Maintenance optimization for wind turbines Ph.D thesis, The Robert Gordon University (2008)
Nilsson, J., Bertling, L.: Maintenance management of wind power systems using condition monitoring systems—life cycle cost analysis for two case studies. IEEE Transactions on Energy Conversion 22(1), 223–229 (2007)
Byon, E., Ding, Y.: Season-dependent condition based maintenance for a wind turbine using a partially observed Markov decision process. IEEE Transactions on Power Systems (2010) (accepted)
Yurdusev, M.A., Ata, R., Cetin, N.S.: Assessment of optimum tip speed ratio in wind turbines using artificial neural networks. Energy 31(12), 2153–2161 (2006)
Jafarian, M., Ranjbar, A.M.: Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine. Renewable Energy 35(9), 2008–2014 (2010)
Hameed, Z., Hong, Y.S., Cho, Y.M., Ahn, S.H., Song, C.K.: Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews 13(1), 1–39 (2009)
Marble, S., Morton, B.: Predicting the remaining life of propulsion system bearings. In: Proceedings of the 2006 IEEE Aerospace Conference, Big Sky, MT, USA (2006)
Kacprzynski, G.J., Roemer, M.J., Modgil, G., Palladino, A., Maynard, K.: Enhancement of physics-of-failure prognostic models with system level features. In: Proceedings of the 2002 IEEE Aerospace Conference, Big Sky, MT, USA (2002)
Li, C.J., Lee, H.: Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing 19, 836–846 (2005)
Banjevic, D., Jardine, A.K.S., Makis, V., Ennis, M.: A control-Limit ploicy and software for condition based maintenance optimization. Infor. 39, 32–50 (2001)
Gebraeel, N., Lawley, M., Li, R., Ryan, J.K.: Life Distributions from Component Degradation Signals: A Bayesian Approach. IIE Transactions on Quality and Reliability Engineering 37(6), 543–557 (2005)
Tian, Z.: An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing (2009) (accepted), doi: 10.1007/s10845-009-0356-9
Tian, Z., Wong, L., Safaei, N.: A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mechanical Systems and Signal Processing 24(5), 1542–1555 (2010)
Tian, Z., Zuo, M.: Health condition prediction of gears using a recurrent neural network approach. IEEE Transactions on Reliability (2010) (accepted)
Wu, B., Tian, Z., Chen, M.: Condition based maintenance optimization using neural network based health condition prediction. In: IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans (2010) (under review)
National instruments products for wind turbine condition monitoring (2010), http://zone.ni.com/devzone/cda/tut/p/id/7676
2008-2009, WindStats Newsletter, 21(4)-22(3), Denmark
Guo, H.T., Watson, S., Tavner, P.: Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation. Reliability Engineering and System Safety 94(6), 1057–1063 (2009)
Fingersh, L., Hand, M., Laxson, A.: Wind turbine design cost and scaling model. National Renewable Energy Laboratory technical report (2006)
Stevens, B.: EXAKT reduces failures at Canadian Kraft Mill, (2006), http://www.modec.com
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Tian, Z., Ding, Y., Ding, F. (2011). Maintenance Optimization of Wind Turbine Systems Based on Intelligent Prediction Tools. In: Nedjah, N., dos Santos Coelho, L., Mariani, V.C., de Macedo Mourelle, L. (eds) Innovative Computing Methods and Their Applications to Engineering Problems. Studies in Computational Intelligence, vol 357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20958-1_4
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