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Journal of Electrical and Computer Engineering
Volume 2012, Article ID 479696, 19 pages
http://dx.doi.org/10.1155/2012/479696
Research Article

Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

1Department of Electrical and Electronics Engineering, School of Electrical and Electronics Engineering, SASTRA University, Tirumalaisamudram, Tamil Nadu, Thanjavur 613 401, India
2W.S. Test Systems Limited, 27th km Bellary Road, Doddajalla Post, Karnataka, Bangalore 562 157, India
3School of Humanities and Sciences, SASTRA University, Tirumalaisamudram, Tamil Nadu, Thanjavur 613 401, India

Received 29 December 2011; Accepted 22 June 2012

Academic Editor: Raj Senani

Copyright © 2012 S. Venkatesh 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.

How to Cite this Article

S. Venkatesh, S. Gopal, and K. Kannan, “Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies,” Journal of Electrical and Computer Engineering, vol. 2012, Article ID 479696, 19 pages, 2012. https://doi.org/10.1155/2012/479696.