Analysis of partial discharge measurement data using a support vector machine

Nur Fadilah Ab Aziz, L. Hao, P. L. Lewin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

This paper investigates the recognition of partial discharge sources by using a statistical learning theory, Support Vector Machine (SVM). SVM provides a new approach to pattern classification and has been proven to be successful in fields such as image identification and face recognition. To apply SVM learning in partial discharge classification, data input is very important. The input should be able to fully represent different patterns in an effective way. The determination of features that describe the characteristics of partial discharge signals and the extraction of reliable information from the raw data are the key to acquiring valuable patterns of partial discharge signals. In this paper, data obtained from experiment is carried out in both time and frequency domain. By using appropriate combination of kernel functions and parameters, it is concluded that the frequency domain approach gives a better classification rate.

Original languageEnglish
Title of host publication2007 5th Student Conference on Research and Development, SCORED
DOIs
Publication statusPublished - 01 Dec 2007
Event2007 5th Student Conference on Research and Development, SCORED - Selangor, Malaysia
Duration: 11 Dec 200712 Dec 2007

Other

Other2007 5th Student Conference on Research and Development, SCORED
CountryMalaysia
CitySelangor
Period11/12/0712/12/07

Fingerprint

learning theory
experiment
learning
Support vector machine
Frequency domain
time
Statistical learning
Kernel
Learning theory
Face recognition
Experiment
Machine learning

All Science Journal Classification (ASJC) codes

  • Education
  • Management Science and Operations Research

Cite this

Ab Aziz, N. F., Hao, L., & Lewin, P. L. (2007). Analysis of partial discharge measurement data using a support vector machine. In 2007 5th Student Conference on Research and Development, SCORED [4451430] https://doi.org/10.1109/SCORED.2007.4451430
Ab Aziz, Nur Fadilah ; Hao, L. ; Lewin, P. L. / Analysis of partial discharge measurement data using a support vector machine. 2007 5th Student Conference on Research and Development, SCORED. 2007.
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Ab Aziz, NF, Hao, L & Lewin, PL 2007, Analysis of partial discharge measurement data using a support vector machine. in 2007 5th Student Conference on Research and Development, SCORED., 4451430, 2007 5th Student Conference on Research and Development, SCORED, Selangor, Malaysia, 11/12/07. https://doi.org/10.1109/SCORED.2007.4451430

Analysis of partial discharge measurement data using a support vector machine. / Ab Aziz, Nur Fadilah; Hao, L.; Lewin, P. L.

2007 5th Student Conference on Research and Development, SCORED. 2007. 4451430.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - This paper investigates the recognition of partial discharge sources by using a statistical learning theory, Support Vector Machine (SVM). SVM provides a new approach to pattern classification and has been proven to be successful in fields such as image identification and face recognition. To apply SVM learning in partial discharge classification, data input is very important. The input should be able to fully represent different patterns in an effective way. The determination of features that describe the characteristics of partial discharge signals and the extraction of reliable information from the raw data are the key to acquiring valuable patterns of partial discharge signals. In this paper, data obtained from experiment is carried out in both time and frequency domain. By using appropriate combination of kernel functions and parameters, it is concluded that the frequency domain approach gives a better classification rate.

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Ab Aziz NF, Hao L, Lewin PL. Analysis of partial discharge measurement data using a support vector machine. In 2007 5th Student Conference on Research and Development, SCORED. 2007. 4451430 https://doi.org/10.1109/SCORED.2007.4451430