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Journal of Electrical and Computer Engineering
Volume 2012, Article ID 479696, 19 pages
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.


Partial discharge (PD) is a major cause of failure of power apparatus and hence its measurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination of multisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization) and unlabelled (K-means) versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.