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Application of data mining on partial discharge part I: predictive modelling classification

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3 Author(s)
Lai, K.X. ; Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW, Australia ; Phung, B.T. ; Blackburn, T.R.

Innovations in computer technology have made possible continuous on-line monitoring of partial discharge (PD) activities. The power industry aims to assess the condition of power system equipment through on-line monitoring of PD activities. This involves long-term continuous data recording and it is very difficult to extract useful information from such a large amount of raw data, particularly if it is done manually. Instead, data mining can be applied in solving this problem. Data mining can be categorized into predictive modelling and descriptive modelling. In this paper, work was mainly focused on predictive data mining, which is classification of PD. The back propagation neural network (BPN), self-organizing map (SOM) and support vector machine (SVM) were used for classification and compared. Results indicate SVM is the best method in terms of classification accuracy and processing speed.

Published in:

Dielectrics and Electrical Insulation, IEEE Transactions on  (Volume:17 ,  Issue: 3 )

Date of Publication:

June 2010

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