Applying Swarm Intelligence and Bayesian Inference for Wind Turbine SCADA-Based Condition Monitoring and Prognostics
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Abstract
Diagnosis and prognosis of potential faults is crucial to maintain and improve the efficiency of the wind energy system. In this paper, we propose a SCADA-based condition monitoring and prognostics system. We apply particle swarm optimization to recognize different patterns of turbine health condition by fusing performance test results. As monitoring daily turbine health condition, we design a data-driven Bayesian inference approach to predict turbine potential failures by tracking the abnormal variations.
How to Cite
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wind energy, Bayesian inference, Fault Diagnosis and Prognosis, data-driven method, PSO
W. Yang, P. Tavner, C. Crabtree, and M. Wilkinson, Cost-effective condition monitoring for wind turbines, IEEE Transactions on Industrial Electronics, vol. 57, pp. 263- 271, Jan. 2010.
C. Crabtree, Survey of commercially available condition monitoring systems for wind turbines, SuperGen Wind, November 2010.
S. Sheng, F. Oyague and S. Butterfield, Investigation of various wind turbine condition monitoring techniques, 7th International Workshop on Structural Health Monitoring, Stanford University, Stanford, Sept. 2009.
A. Zaher, S. McArthur, D. Infield, and Y. Patel, Online wind turbine fault detection through automated SCADA data analysis, Wind Energy, vol. 12, no. 6, pp. 574-593, Sept. 2009.
A. Kusiak and A. Verma, Monitoring wind farms with performance curves, IEEE Transactions on Sustainable Energy, vol. 4, no. 1, January 2013.
J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942-1948, 1995.
W. Lu and F. Chu, Condition monitoring and fault diagnostics of wind turbines, in Proceedings of IEEE Prognostics and System Health Management Conference, 2010.
Z. Chen, X. Lian, H. Yu and Z. Bao, Algorithm of data mining and its application in fault diagnosis for wind turbine, 2nd International Symposium on Knowledge Acquisition and Modeling, vol. 2, pp. 240-243, Nov. 2009.
A. Zaher and S. McArthur, A multi-agent fault detection system for wind turbine defect recognition and diagnosis, IEEE Lausanne of Power Tech., vol. 57, pp.
22-27, July. 2007.
X. Ye, K. Veeramachaneni, Y. Yan and L. A. Osadciw,Unsupervised learning and fusion for failure detection in wind turbines, in Proceedings of 12th International Conference on Information Fusion, July 2009.
Lisa. A. Osadciw, Y. Yan, X. Ye, G. Benson and E. White,Wind turbine diagnostics based on power curve using particle swarm optimization, in Book Wind Power Systems: Applications of Computational Intelligence, Springer, 2010.
B. D. Chen, Survey of commercially available SCADA data analysis tools for wind turbine health monitoring, Technical Report of SuperGen Wind EPSRC Project, Nov. 2010.
W. G. Garlick, R. Dixon and S. J. Watson, A model-based approach to wind turbine condition monitoring using SCADA data, in Proceedings of 20th International Conference on Systems Engineering, 2009.
Z. Hameed, Y. S. Hong, Y. M. Cho, S. H. Ahn, and C. K .Song, Condition monitoring and fault detection of wind turbines and related algorithms: a review, Renewable and Sustainable Energy Reviews, vol. 13, pp. 1-39, 2009.
S. F. Cheng and M. G. Pecht, Multivariate state estimation technique for remaining useful life prediction of electronic products, in Proceedings of AAAI Artificial Intelligent Prognostics, 2007.
Schwabacher, M., & Goebel, K. F. (2007). A survey of artificial intelligence for prognostics, in Proceedings of AAAI Fall Symposium, November 9–11, Arlington, VA.
T. Wang, J. Yu, D. Siegel, J. Lee, A similarity-based prognostics approach for remaining useful life estimation of engineered systems, in Proceedings of International Conference on Prognostics and Health Management, 2008.
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