Analysis of partial discharge measurement data using a support vector machine

• Main Authors: Aziz N.F.A., Hao L., Lewin P.L.
• Other Authors: 57221906825
• Format: Conference Paper
• Published: 2023
• Subjects: On-line monitoring; Partial discharge; Partial discharge classification; Pattern recognition; Support vector machine; Classification (of information); Discharge (fluid mechanics); Electric machine insulation; Face recognition; Feature extraction; Fluid mechanics; Frequency domain analysis; Image retrieval; Learning systems; Research and development management; Vectors; Partial discharges

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. �2007 IEEE.