Some Prognostic and Diagnostic Methods for Determining Wind Turbine Failures - A Review

Yıl 2017, Cilt: 10 Sayı: 2, 168 - 183, 22.12.2017

Cem Emeksiz , Zafer Doğan , Mehmet Akar , Mahmut Hekim

Öz

Yenilenebilir enerji
kaynakları arasında yer alan rüzgar enerjisi son yıllarda dünya tarafından
desteklenmektedir. Özellikle yüksek enerji üretim kapasitesinden dolayı rüzgar
çiftliklerinin sayısı gün geçtikçe artmaktadır. Fakat rüzgar türbinlerinde
bakım maliyetleri; üretim maliyeti, lojistik, tesisat, faz tutarlılığı ve
kontrol vb. nedenlerden dolayı yüksektir.
Prognostikler, uygun maliyetli bir bakım stratejisi ile yüksek kaliteli
tasarımı geliştirmek için uygulanmaktadır. Bu çalışmada rüzgar türbinlerinde
meydana gelen temel hatalar gözden geçirilmiş ve prognostik yaklaşımlar
tartışılmıştır. Bu inceleme bıçak arızaları, jeneratör arızaları, dişli
kutuları ve yaw sistemi arızaları üzerine odaklanmıştır. Ayrıca, bu çalışmada
teknik prognezin mühendislik alanında hızla ilerleyen bir alan olduğu
görülmüştür.

Anahtar Kelimeler

Rüzgar türbinleri, Rüzgar türbini arızaları

Some Prognostic and Diagnostic Methods for Determining Wind Turbine Failures - A Review

Öz

Wind power situated in renewable energy resources has been supported by
the world in recent years. Especially Number of wind farm is increasing because
of the high energy production capacity day by day. However, maintenance costs
in wind turbines are high because of the cost of production, logistics, installation,
phase consistence and control etc. Prognostics are applied to develop
high-quality design with a cost-effective maintenance strategy. In this study
the basic faults of occurring in wind turbines were reviewed and prognostic
approaches were discussed. This review focused on blade failures, generator
failures, gearboxes and yaw system failures.  Moreover, in this study were seen that
technical prognez is a rapidly advancing field in engineering.

Anahtar Kelimeler

Wind turbines, Failures of wind turbine, Prognostic approaches

Kaynakça

• Abadi, M.B., Cruz, S.M.A., Gonçalves, A.P., Gonçalves, P.F.C., Mendes, A.M.S., Ribeiro, A., Silva, F. 2014. Detection of Stator and Rotor Faults in a DFIG Based on the Stator Reactive Power Analysis. IEEE Trans. Industrial Electronics, pp. 2037-2043, Feb.
• Amirat, Y., Choqueuse, V., Benbouzid, M.E.H. 2010. Wind turbines condition monitoring and fault diagnosis using generator current amplitude demodulation. Proceedings of IEEE Energy Conference and Exhibition (EnergyCon), pp. 310-315, Dec.
• Attoui, I., Omeiri, A. 2014. Contribution to the Fault Diagnosis of a Doubly Fed Induction Generator for a Closed-loop Controlled Wind Turbine System Associated with a Two-level Energy Storage System. Electric Power Components and Systems, Vol. 42, pp. 1727-1742, Oct.
• Bennouna, O., Heraud, N., Camblong, H., Rodriguez, M. 2005. Diagnosis of the doubly-fed induction generator of a wind turbine. Wind Engineering, Vol. 29, no. 5, pp. 431-448, Sept.
• Bennouna, O., Heraud, N., Rodriguez, M., Camblong, H. 2007. Data reconciliation and gross error detection applied to wind power. Proc.Inst. Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Vol. 221, no. 3, pp. 497-506, May.
• Bennouna, O., Héraud, N., Camblong, H., Rodriguez, M., Kahyehl, M.A. 2009. Diagnosis and fault signature analysis of a wind turbine at a variable speed. Proc. Inst. Mechanical Engineers, Part O: Journal of Risk and Reliability, Vol. 223, no. 1, pp. 41-50, Mar.
• Boger, L., Wichmann, M.H.G., Meyer, L.O., Schulte, K. 2008. Load and health monitoring in glass fibre reinforced composites with an electrically conductive nano composite epoxy matrix. Composites Science and Technology, Vol. 68, no. 7-8, pp. 1886-1894, Jun.
• Bouthaina, A., Diana, F., Moamar, S.M., Houari, T., Bruno, F., Nicolas, G. 2014. Fault Diagnosis Methods for Wind Turbines Health Monitoring: a Review. European Conference Of The Prognostıcs and Health Management Socıety, pp. 1-8.
• Bouno, T., Yuji, T., Hamada, T., Hideaki, T. 2005. Failure forecast diagnosis of small wind turbine using acoustic emission sensor. KIEE Int. Trans. Electrical Machineary and Energy Conversion Systems, Vol. 5-B, no. 1, pp. 78-83.
• Byington, C.S., Roemer, M.J., Kacprzynski, G.J., Drive, T.P. 2002. Prognostic Enhancements to Diagnostic Systems for Improved Condition-Based Maintenance. 1. In Aerospace Conference Proceedings, Vol.6, pp. 2815-2825, Mar.
• Caithness Wind Farm Information Forum. 2012. Summary of Wind Turbine Accident data to 31 December 2011. Jan.
• Chelidze, D., Cusumano, J. 2004. A dynamical systems approach to failure prognosis. Journal of Vibration and Acoustics, Vol. 126, no.1, pp. 2 – 8, Feb.
• Chen, C., Sun, C., Zhang, Y., Wang, N. 2005. Fault diagnosis for large scale wind turbine rolling bearing using stress wave and wavelet analysis. in Proc. 8th Int. Conf. on Electrical Machines and Systems, Vol.3, pp. 2239-2244, Sep.
• Chong, W.T., Pan, K.C., Poh, S.C., Fazlizan, A., Oon, C.S., Badarudin, A., Nik-Ghazali, N. 2013. Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application. Renewable Energy, Vol. 51, pp. 388–397, Mar.
• Daneshi-Far, Z., Capolino, G.A., Henao, H. 2010. Review of failures and condition monitoring in wind turbine generators. in Proc. XIX International Conference on Electrical Machines - ICEM 2010; Rome, pp. 1-6.
• Dicorato, M., Forte, G., Trovato, M. 2012. Wind farm stability analysis in the presence of variable-speed generators. Energy, Vol. 39, no.1, pp. 40–47.
• Doğan, Z. 2012. Ayrıklaştırma Yöntemleri ve Yapay Sinir Ağı Kullanarak Asenkron Motorlarda Arıza Teşhisi. Doktora Tezi, Marmara Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, 168s, (in Turkish).
• Durovic, S., Wiiliamson, S., Yang, W., Tavner, P. 2009. Condition monitoring artifacts for detecting winding faults in wind turbine DFIGs. in Proc. EWEC 2009, Scientific Track – Wind Turbine Electrical Systems & Components, Marseille, France, pp. 16-19, Mar.
• Eker, B. 2009. Maintenance engineering applications in wind turbines. Engineer and Machine, Vol: 50, no: 598, pp. 66-69. Faiz, J., Ojaghi, M. 2009. Different Indexes for Eccentricity Faults Diagnosis in Three- phase Squirrel-cage Induction Motors: A Review. Mechatronics, Vol. 19, no. 1, pp. 2–13, Feb.
• Farahani, E.M., Hosseinzadeh, N., Ektesabi, M. 2012. Comparison of fault-ride-through capability of dual and single-rotor wind turbines. Renewable Energy, Vol. 48, pp. 473–481, Dec.
• Feng, Z., Liang, M. 2014. Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding. Journal of Soundand Vibration, Vol. 333, no. 20, 5196–5211, Sep.
• Graham, L., Susumu, T., Takanori, U. 2013. Application of CFD for Turbulence Related Operational Risks Assessment of Wind Turbines in Complex Terrain. EWEA 2013; Conference Proceedings, Feb.
• Groer, P. 2000. Analysis of time-to-failure with a weibull model. Proceedings of the Maintenance and Reliability Conference 2000; pp. 59.01 - 59.04.
• Guillaume, B. 2014. Some Diagnostic and Prognostic Methods for Components Supporting Electrical Energy Management in a Military Vehicle. European Conference of The Prognostıcs and Health Management Socıety, ISSN 2153-2648, pp. 1-4, Jul.
• Hall John, F., Dongmei, C. 2012. Performance of a 100kW wind türbine with a Variable Ratio Gearbox. Renewable Energy, Vol. 44, pp.261–6, Aug.
• Hameed, Z., Hong, Y.S., Cho, Y.M., Ahn, S.H., Song, C.K. 2009. 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, Jan.
• Harman, J. 2014. Wind Turbine Optimization. presented at Maintenance and Repair Summit Toronto, 8-9, Canada, Dec.
• Heng, A., Zhang, S., Tan, A.C., Mathew, J. 2009. Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, Vol. 23, no. 3, pp. 724 – 739, Apr.
• Huang, Q., Jiang, D., Hong, L., Ding, Y. 2008. Application of wavelet neural networks on vibration fault diagnosis for wind turbine gearbox. Lecture Notes in Computer Science, Vol. 5264 LNCS, n PART 2, Advances in Neural Networks, pp. 313-320.
• Hyers, R.W., McGowan, J.G., Sullivan, K.L., Manwell, J.F., Syrett, B.C. 2006. Condition monitoring and prognosis of utility scale wind turbines. Energy Materials, Vol. 1, no. 3, pp. 187-203, Sep.
• IAS Motor Reliability Working Group. 1985. Report of Large Motor Reliability Survey of Industrial and Commercial Installations: Part I and Part II. IEEE
• Transactions on Industry Applications, Vol. 21 no. 4, pp. 853 – 872, Jul./Aug.
• Igarashi, T., Hamada, H. 1982. Studies on the vibration and sound of defective roller bearings (First report: vibration of ball bearing with one defect). Bull JSME, Vol. 25, no. 204, pp. 994–1001, Jun.
• Isermann, R. 1984. Process Fault Detection Based on Modeling and Estimation Methods A Survey. Automatica, Vol. 20, no. 4, pp. 387-404, Jul.
• I. S. O. Condition monitoring and diagnostics of machines ISO. 13374-1. Standard. 2003.
• Jardine, A.K., Lin, D., Banjevic, D. A. 2006. Review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, Vol. 20, no. 7, pp. 1483 – 1510, Oct. 2006.
• Keller, A., Perera, U., Kamath, A. 1982. Reliability analysis of cnc machine tools. Reliability Engineering, Vol. 3, no. 6, pp. 449 – 473, Nov.
• Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S., Fleming, P. 2011. Use of SCADA data for failure detection in wind turbines. In International Conference on Energy Sustainability, pp. 2071-2079, Aug.
• Krupa, M. 2012. Methods of Technical Prognosis – Review. IX Internatıonal Congress Machinеs, Technolоgies, Materials, September 19 –21 2012 Varna, Bulgaria.
• Küçük, M., Çetin, N.S., Emeksiz, C. 2012. Stress analysis of shape memory alloys used in wind turbine blade root connection. Energy Education Science and Technology Part A: Energy Science and Research, Vol. 30, SI.1, pp. 667-676, Dec.
• Lebold, M., Thurston, M. 2001. Open standards for condition-based maintenance and prognostic systems. presented at MARCON 2001, 5th AnnualMaintenance and Reliability Conference, Gallinburg, USA.
• Lee Jong, W., Lee Jun, S., Han Jae, H., Shin Hyung, K. 2012. Aeroelastic analysis of wind turbine blades based on modified strip theory. J. Wind Eng. Ind. Aerodyni, Vol. 110, pp.62–69, Nov.
• Lekou, D.J., Mouzakis, F., Anastasopoulos, A., Kourousis, D. 2009. Emerging techniques for health monitoring of wind turbine gearboxes and bearings. in Proc. EWEC 2009; Scientific Track – Operation and Maintenance, Marseille, France, Mar. 16-19.
• Lıang, T., Decastro, J., Kacprzynskı, G., Goebel, K., Vachtsevanos, G. 2010. Filtering and prediction techniques for model-based prognosis and uncertainty management. Prognostics and Health Management Conference 2010; PHM, pp. 1-10. E-ISBN: 978-1-4244-4758-9, ISBN: 978-1-4244-4756-5, Jan.
• Liu, W.Y., Tang, B.P., Han, J.G., Lu, X.N., Hua, N.N., He, Z.Z. 2015. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews, Vol. 44, pp. 466–472, Jan.
• Lu, Q., Breikin, T., Wang, H. 2011. Modelling and Fault Diagnosis of Stator Inter-Turn Short Circuit in Doubly Fed Induction Generators. Preprints of the 18th IFAC World Congress 2011, pp. 1013-1018, Aug.28- Sep.2.
• Luo, J., Namburu, M., Pattıpatı, K., Qıauıo, L. 2003. Model-based Prognostic techniques. AUTOTESTCON 2003, IEEE Systems Readiness Technology Conference; Proceedings. September, pp. 330-340. ISSN : 1080-7725, Print ISBN: 0-7803-7837-7.
• Luo, J., Pattipati, K., Qiao, R.L., Chigusa, S. 2008. Model-based prognostic techniques applied to a suspension system. Transactions on Systems, Man, and Cybernetics, Vol. 38, no.5, pp. 1156–1168, Sept.
• Luo, J., Tu, F., Azama, S.M., Pattıpatı, K.R., Wıllett, P.K. 2003. Intelligent model-based diagnostics for vehicle health management. Proc. SPIE, Vol. 5107, no. 3, pp. 13-26. ISSN : 0277-786X, Sep.
• Ma, Y., He, C., Feng, X. 2012. Institutions Function and Failure Statistic and Analysis of Wind Turbine. Physics Procedia, Vol. 24, pp.25–30.
• Maintenance Terminology, EN. 13306., 2001.
• Nandi, S., Toliyat, H.A., Li, X. 2005. Condition Monitoring and Fault Diagnosis of Electrical Machines-A Review. IEEE Transactions on Energy Conversion, Vol. 20, no. 4, pp. 719-729, Dec.
• Penicuik Environment Protection Association (PEPA). Blade loss or failure. unpublished.
• Rademakers, L.W.M.M., Seebregts, A.J., Hom, B.A.V.D., Jehee, J.N.T., Blok, B.M. 1993. Methodology for probabilistic safety assessment of wind turbines. PSA of Wind Turbine, ECN-C-93-010, Mar.
• Ribrant, J., Bertling, L.M. 2007. Survey of Failures in Wind Power Systems with Focus on Swedish Wind Power Plants during 1997&2005. IEEE Trans. Energy Conversion, Vol. 22, no.1, pp. 167-173, Mar.
• Rumsey, M.A., Paquette, J.A. 2008. Structural health monitoring of wind turbine blades. in Proc. SPIE, Smart Sensor Phenomena, Technology, Networks, and Systems, Vol. 6933, p. 69330E. Apr.
• Saha, B., Goebel, K., Poll, S., Chrıstopherson, J. 2009. Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework. IEEE Transactions On Instrumentation and Measurement, Vol. 58, no.2, pp. 291 -296, Feb.
• Schwabacher, M.A. 2005. A Survey of Data-Driven Prognostics. American Institute of Aeronautics and Astronautics, pp.1-5, AIAA-2005-7002. Sep.
• Soua, S., Van Lies Hout, P., Perera, A., Gan, T.H., Bridge, B. 2013. Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as apre-requisite for effective condition monitoring. Renewable Energy, Vol. 51, pp. 175–181, Mar.
• Stubkier, S., Dyrbye, E. 2011. AVN Energy Designs a New Hydraulic Yaw System and Reduces the Fatigue Loads of The Wind Turbine. Modern Energy Review, Vol. 3, no. 2, pp. 45.
• Tang, B., Song, T., Li, F., Deng, L. 2014. Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renewable Energy, Vol. 62, pp. 1–9, Feb.
• Tavner, P.J., Xiang, J., Spinato, F. 2006. Reliability Analysis for Wind turbines. Wind Energy, Vol. 10, pp. 1-18, Jul.
• Tsai, C.S., Hsieh, C.T., Huang, S.J. 2006. Enhancement of damage detection of wind turbine blades via CWT-based approaches. IEEE Trans. Energy Conversion, Vol. 21, no. 3, pp. 776-781, Sep.
• Vachtsevanos, G., Lewıs, F., Roemer, M., Hess, A., Wu, B. 2006. Intelligent Fault Diagnosis and Prognosis for Engineering Systems. John Wiley & Sons, Inc. New Jersey, Pages 434. ISBN:978-0-471-72999-0.
• Vachtsevanos, G., Lewis, F.L., Roemer, M., Hess, A., Wu, B. 2007. Intelligent fault diagnosis and prognosis for engineering systems. John Wiley & Sons, Inc. 2006; ISBN: 978-0-471-72999-0, Published Online: 23 MAR.
• Venkat, V., Raghunathan, R. 2003. A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers & Chemical Engineering, Vol. 27, no.3, pp. 293–311, Mar.
• Voisin, A., Levrat, E., Cocheteux, P., Iung, B. 2010. Generic prognosis model for proactive maintenance decision support: application to pre-industrial e-maintenance test bed. Journal of Intelligent, Vol. 21, no. 2, pp.177-193, Apr.
• Volanthen, M. 2007. Blade and rotor condition monitoring using blade load measurement data. in Proc. 2007 Non-Grid-Connected Wind Power Systems, pp. 468-473, Shanghai.
• Wang, Z., Guo, Q. 2007. The Diagnosis method for converter fault of the variable speed wind turbine based on the neural Networks. in Proc. 2nd Int. Conf. on Innovative Computing, Information and Control, Kumamoto, Japan, pp. 615-618, Sep.
• Watson, S.J., Xiang, J. 2006. Real-time condition monitoring of offshore wind turbines. in Proc. EWEC Paper #BL-3.
• Watson, S.J., Xiang, B.J., Wenxian, Y., Tavner, P.J. 2010. Condition Monitoring of the Power Output of Wind Turbine Generators using Wavelets. IEEE Trans. Energy Conversion, Vol. 25, pp. 715-721, Sept.
• Wilkinson, M.R., Spinato, F., Tavner, P.J. 2007. Condition monitoring of generators and other subassemblies in wind turbine drive trains. in Proc. IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp. 388-392, Sep.
• Yang, S., Li, W., Wang, C. 2008. The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network. Proc. Int. Conf. on Condition Monitoring and Diagnosis, pp. 1327-1330.
• Yang, W., Tavner, P.J., Crabtree, C.J., Wilkinson, M. 2008. Research on a simple, cheap but globally effective condition monitoring technique for wind turbines. In International Conference on Electrical Machines, pp. 1-5, Sep.
• Ye, L., Sun, H., Song, X., Li, L. 2012. Dynamic modeling of a hybrid wind/ solar/ hydro micro grid in EMTP/ATP. Renewable Energy, Vol. 39, no.1, pp. 96–106, Mar.
• Yonggang, L., Le, T., Hongwei, L., Wei, L. 2016. Fault analysis of wind turbines in China. Renewable and Sustainable Energy Reviews, Vol. 55, pp. 482–490, Mar.
• Yuji, T., Bouno, T., Hamada, T. 2006. Suggestion of temporarily for forecast diagnosis on blade of small wind turbine. IEEJ Trans.Power and Energy, Vol. 126, no. 7, pp. 710-711, Oct.
• Zhang, Y., Jiang, J. 2008. Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control, Vol. 32, no.2, pp. 229–252, Mar.