Descriptive Data Mining of Partial Discharge Using Decision Tree With Genetic Algorithm

Partial discharge (PD) is a common phenomenon that occurs in insulation of high voltage equipment such as transformers and has a damaging effect to the insulation. In this paper, the application of descriptive data mining on PD occurring in insulating systems is shown. Experiments were set up to cre...

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Bibliographic Details
Published inAustralian Journal of Electrical & Electronics Engineering Vol. 6; no. 3; pp. 249 - 259
Main Authors Lai, K.X., Phung, B.T., Blackburn, T.R.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Routledge 01.01.2009
Subjects
corona
data mining
decision tree
descriptive data mining
Electric discharges
Electric insulators and insulation
Electrical engineering
genetic algorithm
internal discharge
Neural networks (Computer science)
Partial discharge
phase resolved analysis
pulse height analysis
surface discharge
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Summary:Partial discharge (PD) is a common phenomenon that occurs in insulation of high voltage equipment such as transformers and has a damaging effect to the insulation. In this paper, the application of descriptive data mining on PD occurring in insulating systems is shown. Experiments were set up to create three basic types of PD: corona, surface discharges and internal discharges. PD data were analysed using phase resolved analysis and pulse height analysis. Descriptive data mining was applied on the collected data using decision tree with genetic algorithm (GA) to mine the rules/relationships that can be used to differentiate the PD. These extracted rules are useful as input to predictive data mining such as fuzzy logics.
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Australian Journal of Electrical & Electronics Engineering, Vol. 6, No. 3, 2009: 249-259
ISSN:1448-837X
2205-362X
DOI:10.1080/1448837X.2009.11464243