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Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 431357, 9 pages
http://dx.doi.org/10.1155/2011/431357
Research Article

Adaptive Neurofuzzy Inference System-Based Pollution Severity Prediction of Polymeric Insulators in Power Transmission Lines

1Department of Electrical Engineering, K. S. Rangasamy College of Technology, Tiruchengode 637 215, India
2Department of Electrical Engineering, SonaPERT R&D Centre, Sona College of Technology, Salem 636 005, India

Received 11 January 2011; Revised 13 April 2011; Accepted 16 June 2011

Academic Editor: Christian Mayr

Copyright © 2011 C. Muniraj and S. Chandrasekar. 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. J. S. T. Looms, Insulators for High Voltages, IEE Series, 1990.
  2. R. S. Gorur, E. A. Cherney, and J. T. Burnham, Outdoor Insulators, Ravi S. Gorur, Phoenix, Ariz, USA, 1999.
  3. R. Dass, “Grid disturbance in India on 2nd January 2001,” Electra, no. 196, pp. 6–15, 2001.
  4. “CEA enquiry committee report of Grid incident of Northern region,” 2007.
  5. S. Kumagai and N. Yoshimura, “Leakage current characterization for estimating the conditions of ceramic and polymeric insulating surfaces,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 11, no. 4, pp. 681–690, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Suda, “Frequency characteristics of leakage current waveforms of an artificially polluted suspension insulator,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 8, no. 4, pp. 705–709, 2001. View at Publisher · View at Google Scholar · View at Scopus
  7. B. S. Reddy and G. R. Nagabhushana, “Study of leakage current behaviour on artificially polluted surface of ceramic insulator,” Plasma Science and Technology, vol. 5, no. 4, pp. 1921–1926, 2003. View at Scopus
  8. R. Sarathi and S. Chandrasekar, “Diagnostic study of the surface condition of the insulation structure using wavelet transform and neural networks,” Electric Power Systems Research, vol. 68, no. 2, pp. 137–147, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Chandrasekar and C. Kalaivanan, “Investigations on harmonic contents of leakage current of porcelain insulator under polluted conditions,” in Proceedings of the Fifteenth National Power Systems Conference (NPSC), pp. 340–344, 2008.
  10. P. Cline, W. Lannes, and G. Richards, “Use of pollution monitors with a neural network to predict insulator flashover,” Electric Power Systems Research, vol. 42, no. 1, pp. 27–33, 1997. View at Scopus
  11. V. T. Kontargyri, A. A. Gialketsi, G. J. Tsekouras, I. F. Gonos, and I. A. Stathopulos, “Design of an artificial neural network for the estimation of the flashover voltage on insulators,” Electric Power Systems Research, vol. 77, no. 12, pp. 1532–1540, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Al Alawi, M. A. Salam, A. A. Maqrashi, and H. Ahmad, “Prediction of flashover voltage of contaminated insulator using artificial neural networks,” Electric Power Components and Systems, vol. 34, no. 8, pp. 831–840, 2006.
  13. S. A. Ahmad, P. S. Ghosh, S. S. Ahmed, and S. A.-K Aljunid, “Assessment of ESDD on high voltage insulators using artificial neural network,” Electic Power Systems Research, vol. 72, pp. 131–136, 2004.
  14. J. Li, C. Sun, W. Sima, Q. Yang, and J. Hu, “Contamination level prediction of insulators based on the characteristics of leakage current,” IEEE Transactions on Power Delivery, vol. 25, no. 1, Article ID 5345710, pp. 417–424, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. IEC 60507, “Artificial pollution tests on high voltage insulators to be used on AC systems,” 1991.
  16. S. C. Oliveira, E. Fontana, and F. J. D. M. de Melo Cavalcanti, “Leakage current activity on glass-type insulators of overhead transmission lines in the ortheast region of Brazil,” IEEE Transactions on Power Delivery, vol. 24, no. 2, pp. 822–827, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Haykin, Neural Networks: A comprehensive Foundation, Prentice Hall, New York, NY, USA, 2nd edition, 1999.
  18. B. Yegnanarayana, Artificial Neural Networks, Prentice Hall of India, 1999.
  19. J. S. R. Jang, “Adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993. View at Publisher · View at Google Scholar · View at Scopus
  20. C. P. Kurian, J. George, I. J. Bhat, and R. S. Aithal, “ANFIS model for the time series prediction of interior daylight illuminance,” Artificial Intelligence & Machine Learning Journal, vol. 6, pp. 35–40, 2006.
  21. J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall of India, 2006.