QUEST tree model for partial discharge classification on high voltage equipment

Chatpattananan, V.; Pattanadech, N.; Vicetjindavat, K.

doi:10.1109/ICPADM.2006.284218

QUEST tree model for partial discharge classification on high voltage equipment

Author(s)

Chatpattananan, V.

Pattanadech, N.

Vicetjindavat, K.

Date Issued

January 1, 2006

Type

Conference Paper

DOI

10.1109/ICPADM.2006.284218

Abstract

This document proposes a statistical classification model using QUEST (Quick, Unbiased, and Efficient Statistical Tree) to classify PD patterns into four categories listed as corona at high voltage side in air, corona at low voltage side in air, surface in air, and internal discharge. The independent variables in this QUEST model are skewness, kurtosis, asymmetry, and cross correlation following the φ-q-n PD patterns obtained from the fingerprint analysis which is a digital signal processing technique for PD measurement. The experiments were set to simulate all four PD patterns to obtain statistical parameters into 10 independent variables from the fingerprint analysis. This QUEST classification model can reduce the number of independent variables used to estimate from 10 independent variables to 3 independent variables with the classification accuracy of 100 percent. © 2006 IEEE.

Citation

Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials, 476-478, 2006