Fault Diagnosis Methods for Wind Turbines Health Monitoring: a Review
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Abstract
Recently, the rapid expansion of wind energy activity has led to an increasing number of publications that deal with wind turbine health monitoring. In real practice, implementing a prognostics and health management (PHM) strategy for wind turbines is challenging. Indeed, wind turbines are complex electro-mechanical systems that often work under rapidly changing environment and operating load conditions. Although several review papers that address wind turbines fault diagnosis were published, they are mostly focused on a specific component or on a specific category of methods. Therefore, a larger snapshot on recent advances in wind turbine fault diagnosis is presented in this paper. Fault diagnosis approaches could be grouped in three major categories according to the available a priori knowledge about the system behavior: quantitative/qualitative model, signal analysis and artificial intelligence based approaches. Each of the proposed methods in the literature has its advantages and drawbacks. Therefore, a comparison between these methods according to some meaningful evaluation criteria is conducted.
How to Cite
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fault diagnosis, Wind turbines, Pattern recognition
Arabian-Hoseynabadi, H., Oraee, H., & Tavner, P. J. (2010). Failure modes and effects analysis (FMEA) for wind turbines. International Journal of Electrical Power & Energy Systems, 32(7), 817-824.
Azarian, M. H., Kumar, R. S., Patil, N., Shrivastava, A., & Pecht, M. (2011). Applications of health monitoring to wind turbines. Proceedings of Condition Monitoring and Diagnostics Engineering Management Conference, pp. 304-313. Chen, W., Ding, S. X., Sari, A., Naik, A., Khan, A., & Yin, S. (2011). Observer-based FDI schemes for wind turbine benchmark. Proceedings of IFAC World Congress, pp. 7073-7078. Echavarria, E., Tomiyama, T., Huberts, H., & Van Bussel, G. (2008). Fault diagnosis system for an offshore wind turbine using qualitative physics. Proceedings of EWEC conference.
Fischer, K., Besnard, F., & Bertling, L. (2012). Reliability-centered maintenance for wind turbines based on statistical analysis and practical experience. IEEE Transactions on Energy Conversion, 27(1), 184-195. Flórez Rodriguez D. M., Sliding Mode Control in Grid-Connected Wind Farms for Stability Enhancement, PhD Thesis, Universidad Carlos III de Madrid, Spain, June 2012. García Márquez, F. P., Tobias, A. M., Pinar Pérez, J. M., & Papaelias, M. (2012). Condition monitoring of wind turbines: Techniques and methods. Renewable Energy, 46, 169-178.
Godwin, J. L., & Matthews, P. (2013). Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis. International Journal of Prognostics and Health Management.
Guo, P. (2011). WEC condition monitoring based on SCADA data analysis. In IEEE Chineese Control Conference (CCC), pp. 5099-5103. 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, 13(1), 1-39.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510.
Kahrobaee, S., & Asgarpoor, S. (2011). Risk-based failure mode and effect analysis for wind turbines (RB-FMEA). In IEEE North American Power Symposium (NAPS), pp. 1-7.
Kalgren, P. W., Byington, C. S., & Roemer, M. J. (2006). Defining PHM, a lexical evolution of maintenance and logistics. In IEEE Autotestcon, pp. 353-358.
Katipamula, S., & Brambley, M. R. (2005). Review article: methods for fault detection, diagnostics, and prognostics for building systems—a review, Part I. HVAC&R Research, 11(1), 3-25.
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. Kostandyan, E. E., & Sørensen, J. D. (2012). Physics of failure as a basis for solder elements reliability assessment in wind turbines. Reliability Engineering & System Safety, 108, 100-107.
Kusiak, A., & Li, W. (2011). The prediction and diagnosis of wind turbine faults. Renewable Energy, 36(1), 16-23.
Kusiak, A., & Verma, A. (2011). A data-driven approach for monitoring blade pitch faults in wind turbines. IEEE Transactions on Sustainable Energy, 2(1), 87-96.
Kusiak, A., & Verma, A. (2013). Monitoring wind farms with performance curves. IEEE Transactions on Sustainable Energy, 4(1), 192-199. Laouti, N., Sheibat-Othman, N., & Othman, S. (2011, August). Support vector machines for fault detection in wind turbines. In Proceedings of IFAC World Congress, pp. 7067-707.
Liu, W. Y., Zhang, W. H., Han, J. G., & Wang, G. F. (2012). A new wind turbine fault diagnosis method based on the local mean decomposition. Renewable Energy, 48, 411-415.
Lu, B., Li, Y., Wu, X., & Yang, Z. (2009). A review of recent advances in wind turbine condition monitoring and fault diagnosis. In Power Electronics and Machines in Wind Applications Conference, pp. 1-7. Lu, B., & Sharma, S. K. (2009). A literature review of IGBT fault diagnostic and protection methods for power inverters. IEEE Transactions on Industry Applications, 45(5), 1770-1777. Nie, M., & Wang, L. (2013). Review of Condition Monitoring and Fault Diagnosis Technologies for Wind Turbine Gearbox. Procedia CIRP, 11, 287-290.
Odgaard, P.F., Stoustrup, J., & Kinnaert, M. (2009). Fault tolerant control of wind turbines- a benchmark model. IFAC symposium on fault detection, supervision and safety of technical processes, 155-160.
Rodriguez, L., Garcia, E., Morant, F., Correcher, A., & Quiles, E. (2008). Application of latent nestling method using coloured petri nets for the fault diagnosis in the wind turbine subsets. In IEEE International Conference on Emerging Technologies and Factory Automation, pp. 767-773.
Roemer, M. J., Nwadiogbu, E. O., & Bloor, G. (2001). Development of diagnostic and prognostic technologies for aerospace health management applications. In IEEE Aerospace Conference, pp. 3139-3147. Sharma, S., & Mahto, D. (2013). Condition monitoring of wind turbines: a review. International Journal of Scientific and Engineering Research, 4(8), 35-50.
Sheng, S. (2011). Investigation of Various Condition Monitoring Techniquest Based on a Damaged Wind Turbine Gearbox. International workshop on structural health monitoring, Stanford, California. Simani, S., Castaldi, P., & Tilli, A. (2011). Data-driven approach for wind turbine actuator and sensor fault detection and isolation. In Proceedings of IFAC World Congress, pp. 8301-8306.
Tong, C., & Guo, P. (2013). Data mining with improved Apriori algorithm on wind generator alarm data. In Chinese Control and Decision Conference (CCDC), pp. 1936-1941.
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis: Part III: Process history based methods. Computers & chemical engineering, 27(3), 327-346.
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis, Part II: Qualitative models and search strategies. Computers in Chemical Engineering 27(3), 313-326.
Verma, A. P. (2012). Performance monitoring of wind turbines: a data-mining approach. Doctoral dissertation. The University of Iowa, Iowa, USA.
Yang, W., Court, R., & Jiang, J. (2013). Wind turbine condition monitoring by the approach of SCADA data analysis. Renewable Energy, 53, 365-376.
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.
Yang, W., Tavner, P. J., Sheng, S., & Court, R. S. (2012). Information Entropy: an effective approach for wind turbine condition monitoring. EWEA conference.
Yongxin, F., Tao, Y., Wenguang, Y., & Dongxiang J. (2012). Study of fault diagnosis method for wind turbine with decision classification algorithms and expert system. TELKOMNIKA Indonesian Journal of Electrical Engineering, 10(5), 905-910.
Zhang, Z., Verma, A., & Kusiak, A. (2012). Fault analysis and condition monitoring of the wind turbine gearbox. IEEE Transactions on Energy Conversion, 27(2), 526-535.
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