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Computational Intelligence and Neuroscience
Volume 2014, Article ID 580972, 13 pages
http://dx.doi.org/10.1155/2014/580972
Research Article

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

School of Electrical and Robotic Engineering, University of Shahrood, P.O. Box 3619995161, Shahrood, Iran

Received 1 October 2013; Accepted 3 February 2014; Published 11 March 2014

Academic Editor: Christian W. Dawson

Copyright © 2014 Nasser Talebi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Blanke, K. Michel, L. Jan, and S. Marcel, Diagnosis and Fault-Tolerant Control, Springer, 2006.
  2. H. C. Cho, J. Knowles, M. S. Fadali, and K. S. Lee, “Fault detection and isolation of induction motors using recurrent neural networks and dynamic bayesian modeling,” IEEE Transactions on Control Systems Technology, vol. 18, no. 2, pp. 430–437, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Rothenhagen and F. W. Fuchs, “Doubly fed induction generator model-based sensor fault detection and control loop reconfiguration,” IEEE Transactions on Industrial Electronics, vol. 56, no. 10, pp. 4229–4238, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Sloth, T. Esbensen, and J. Stoustrup, “Robust and fault-tolerant linear parameter-varying control of wind turbines,” Mechatronics, vol. 21, no. 4, pp. 645–659, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Dolan, Wind turbine modelling, control and fault detection [Ph.D. dissertation], Technical University of Denmark,, Lyngby, Denmark, 2010.
  6. M. A. Kahyeh, N. Héraud, I. S. Guelle, and O. Bennouna, “Fault diagnosis of variable speed wind turbine,” in Proceedings of the 18th Mediterranean Conference on Control and Automation (MED '10), pp. 471–476, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Sloth, T. Esbensen, and J. Stoustrup, “Robust and fault-tolerant linear parameter-varying control of wind turbines,” Mechatronics, vol. 21, no. 4, pp. 645–659, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Donders, V. Verdult, and M. Verhaegen, “Fault detection and identification for wind turbine systems: a closed-loop analysis,” in Proceedings of the 2004 International Conference on Noise and Vibration Engineering (ISMA '04), pp. 2619–2630, September 2004. View at Scopus
  9. A. Verma and A. Kusiak, “Fault monitoring of wind turbine generator brushes: a data-mining approach,” Journal of Solar Energy Engineering, Transactions of the ASME, vol. 134, no. 2, Article ID 021001, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Kusiak and W. Li, “The prediction and diagnosis of wind turbine faults,” Renewable Energy, vol. 36, no. 1, pp. 16–23, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Entezami, S. Hillmansen, P. Weston, and M. Ph Papaelias, “Fault detection and diagnosis within a wind turbine mechanical braking system using condition monitoring.,” Renewable Energy, vol. 47, pp. 175–182, 2012. View at Google Scholar
  12. L. Wenyi, W. Zhenfeng, H. Jiguang, and W. Guangfeng, “Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM,” Renewable Energy, vol. 50, pp. 1–6, 2013. View at Google Scholar
  13. E. Kamal and A. Aitouche, “Robust fault tolerant control of DFIG wind energy systems with unknown inputs,” Renewable Energy, vol. 56, pp. 2–15, 2013. View at Google Scholar
  14. S. M. Tabatabaeipour, P. F. Odgaard, T. Bak, and J. Stoustrup, “Fault detection of wind turbines with uncertain parameters: a set-membership approach,” Energies, vol. 5, no. 7, pp. 2424–2448, 2012. View at Google Scholar
  15. Ozdemir, Ahmet Arda, Peter Seiler, and J. Gary Balas, “Wind turbine fault detection using counter-based residual thresholding,” in Proceedings of IFAC World Congress, pp. 8289–8294, 2011.
  16. 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, no. 1, pp. 1–39, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. P. Caselitz and J. Giebhardt, “Advanced maintenance and repair for offshore wind farms using fault prediction techniques,” in Proceedings of the World Wind Energy Conference, 2002.
  18. I. Hwang, S. Kim, Y. Kim, and C. E. Seah, “A survey of fault detection, isolation, and reconfiguration methods,” IEEE Transactions on Control Systems Technology, vol. 18, no. 3, pp. 636–653, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, “A review of process fault detection and diagnosis part I: quantitative model-based methods,” Computers and Chemical Engineering, vol. 27, no. 3, pp. 293–311, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Patan, Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes, vol. 377, Springer, 2008.
  21. V. Venkatasubramanian, R. Rengaswamy, and S. N. Kavuri, “A review of process fault detection and diagnosis part II: qualitative models and search strategies,” Computers and Chemical Engineering, vol. 27, no. 3, pp. 313–326, 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Jiang, Z. Gao, P. Shi, and Y. Xu, “Adaptive fault-tolerant tracking control of near-space vehicle using TakagiSugeno fuzzy models,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 5, pp. 1000–1007, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. H. M. Hasanien, S. M. Muyeen, and J. Tamura, “Speed control of permanent magnet excitation transverse flux linear motor by using adaptive neuro-fuzzy controller,” Energy Conversion and Management, vol. 51, no. 12, pp. 2762–2768, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. K. Patan and T. Parisini, “Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process,” Journal of Process Control, vol. 15, no. 1, pp. 67–79, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Cannas, G. Celli, M. Marchesi, and F. Pilo, “Neural networks for power system condition monitoring and protection,” Neurocomputing, vol. 23, no. 1–3, pp. 111–123, 1998. View at Publisher · View at Google Scholar · View at Scopus
  26. I. B. Ciocoiu, “Time series analysis using RBF networks with FIR/IIR synapses,” Neurocomputing, vol. 20, no. 1–3, pp. 57–66, 1998. View at Publisher · View at Google Scholar · View at Scopus
  27. T. G. Barbounis and J. B. Theocharis, “Locally recurrent neural networks for long-term wind speed and power prediction,” Neurocomputing, vol. 69, no. 4–6, pp. 466–496, 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. T. G. Barbounis and J. B. Theocharis, “Locally recurrent neural networks for wind speed prediction using spatial correlation,” Information Sciences, vol. 177, no. 24, pp. 5775–5797, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Ayoubi, “Fault dynamic neural structure and application to a turbo-charger,” in Proceedings of the International Symposium on Fault Detection, Supervision and Safety for Technical Processes, pp. 618–623, 1994.
  30. L. M. Ho, “Application of adaptive thresholds in robust fault detection of an electro-mechanical single-wheel steering actuator,” in Proceedings of the Fault Detection, Supervision and Safety of Technical Processes, vol. 8, no. 1, pp. 259–264, 2012.
  31. D. Valh, B. Bratina, and B. Tovornik, “On-line fault detection and isolation using analytical redundancy,” Electrotechnical Review, vol. 73, no. 5, pp. 267–272, 2006. View at Google Scholar · View at Scopus
  32. S. Montes De Oca, V. Puig, and J. Blesa, “Robust fault detection based on adaptive threshold generation using interval LPV observers,” International Journal of Adaptive Control and Signal Processing, vol. 26, no. 3, pp. 258–283, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. F. D. Bianchi, H. de Battista, and R. J. Mantz, Wind Turbine Control Systems, Springer, 2007.
  34. M. O. L. Hansen, Aerodynamics of Wind Turbines, Earthscan, 2008.
  35. A. Junyent-Ferré, O. Gomis-Bellmunt, A. Sumper, M. Sala, and M. Mata, “Modeling and control of the doubly fed induction generator wind turbine,” Simulation Modelling Practice and Theory, vol. 18, no. 9, pp. 1365–1381, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. L. Xu and P. Cartwright, “Direct active and reactive power control of DFIG for wind energy generation,” IEEE Transactions on Energy Conversion, vol. 21, no. 3, pp. 750–758, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. A. A. El-Sattar, N. H. Saad, and M. Z. S. El-Dein, “Dynamic response of doubly fed induction generator variable speed wind turbine under fault,” Electric Power Systems Research, vol. 78, no. 7, pp. 1240–1246, 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. P. C. Krause, O. Wasynczuk, and S. D. Sudhoff, Analysis of Electric Machinery, IEEE Press, 2002.
  39. R. Pena, J. C. Clare, and G. M. Asher, “Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation,” IEE Proceedings-Electric Power Applications, vol. 143, no. 3, pp. 231–241, 1996. View at Google Scholar