A sensor network modeling and fault detection method for large wind farms by using neural networks

IEEE International Conference on Control and Automation, ICCA

Volumn , Issue , 2014, Pages 308-313

  Wang, Qing ^b   Yang, Fan ^b   Ge, Quanbo ^a   Yang, Qinmin ^c  

^a HANGZHOU DIANZI UNIVERSITY   (China)

^b Zhejiang Windey Engineering Co Ltd   (China)

^c STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC UTILITIES; FAULT DETECTION; SENSOR NETWORKS; WIND POWER;

MATHEMATICAL ANALYSIS; MONITORING PURPOSE; NEURAL NETWORK (NN); NONLINEAR FUNCTIONS; SENSOR NETWORK MODELING; SENSOR NETWORK NODES; UNEXPECTED FAILURES; UNIVERSAL APPROXIMATION PROPERTIES;

SENSOR NODES;

EID: 84906547382     PISSN: 19483449     EISSN: 19483457     Source Type: Conference Proceeding    
DOI: 10.1109/ICCA.2014.6870937     Document Type: Conference Paper

References (18)

1. S. Katsura, Y. Matsumoto, and K. Ohnishi, Proc. IEEE Int. Conf. Control Syst. Technol., pp. 796-801, 2003.
2. J. Chen and R. J. Patton, Robust Model-based Fault Diagnosis for Dynamic Systems, Kluwer Academic publishers, MA, USA, (1999).
3. J. Gertler, "Survey of model-based failure detection and isolation in complex plants", IEEE Contr. Syst. Mag., vol. 8, pp. 3-11, (1988).
4. J. A. Silmon, Operational industrial fault detection and diagnosis: railway actuator case studies, Ph. D. dissertation (2009)
5. 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 27 (3) (2003), pp. 293-311.
6. R. Isermann, Model-based fault-detection and diagnosis-Status and applications, Annual Reviews in Control 29 (1) (2005), pp. 71-85.
7. J. A. Farrell and M. M Polycarpou, Adaptive Approximation based Control-Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, Wiley Interscience, NJ, USA, (2006).
8. K. S. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4-27, Mar. (1990).
9. F. L. Lewis, S. Jagannathan and A. Yesilderik, Neural Network Control of Robot Manipulators and Nonlinear Systems, Taylor and Francis, (1999).
10. B. Igelnik and Y. H. Pao, "Stochastic choice of basis functions in adaptive function approximation and the functional-link net," IEEE Trans. Neural Network, vol. 6, no. 6, pp. 1320-1329, Nov. (1995).
11. A.I. Moustapha, R.R. Selmic, "Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection," IEEE Transactions on Instrumentation and Measurement, vol.57, no.5, pp. 981-988, May (2008)
12. B. T. Thumati and S. Jagannathan, "A model based fault detection and prediction scheme for nonlinear multivariable discrete-time systems with asymptotic stability guarantees", IEEE Transactions on Neural Networks, vo. 21, no.3, pp. 404-423, (2010).
13. W. T. Miller, R. S. Sutton, and P. J. Werbos, Neural Networks for Control, Cambridge, MA, MIT Press, (1990).
14. Q. Yang, Y. Sun, Automated Fault Accommodation for Discrete-time Systems Using Online Approximators, Chinese Control Conference, (2011).
15. S. Jagannathan and F. L. Lewis, "Robust implicit self-tuning regulators", Automatica, vol. 32, no. 12, pp. 1629-1644, (1996).
16. D. S. Bernstein, "Sensor performance specifications," IEEE Control Syst. Mag., vol. 21, no. 4, pp. 9-18, (2001).
17. GWEC, "Global wind energy outlook," in Global Wind Energy Council Report, September 2006. [Online]. Available: http://www.gwec.net/
18. Nikolaos Bartzoudis, and Klaus McDonald-Maier. "An embedded sensor validation system for adaptive condition monitoring of a wind farms." IEEE Second NASA/ESA Conference on Adaptive Hardware and Systems, 2007. AHS, 2007.