Application of Fuzzy C-Means Clustering Approach to Partial Discharge Pattern Recognition of Cast-Resin Current Transformers

Author

Chang, Wen-Yeau ; Yang, Hong-Tzer

Author_Institution

Dept. of Electr. Eng., St. John´´s Univ., Taipei

fYear

2006

fDate

38869

Firstpage

372

Lastpage

375

Abstract

This paper proposes a fuzzy c-means (FCM) clustering based recognition method to identify the defects of cast-resin current transformers (CRCT) arising from partial discharge (PD). To identify the defects, field data are collected in this paper using a PD detecting system for the CRCT. The proposed FCM clustering based classifier builds the cluster centers according to distributions of the extracted feature vectors. Effectiveness and feasibility of the proposed method have been verified through the encouraging results obtained using comprehensive experimental data

Keywords

current transformers; fuzzy systems; neural nets; partial discharges; pattern classification; pattern clustering; power engineering computing; power transformer testing; resins; cast-resin current transformer; extracted feature vector; fuzzy c-means clustering approach; partial discharge pattern recognition; Artificial neural networks; Circuit testing; Clustering algorithms; Current transformers; Data mining; Epoxy resins; Feature extraction; Partial discharges; Pattern recognition; Power transformer insulation; Cast-Resin Current Transformers; Fuzzy C-Means Clustering; Partial Discharge; Pattern Recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Properties and applications of Dielectric Materials, 2006. 8th International Conference on

Conference_Location

Bali

Print_ISBN

1-4244-0189-5

Electronic_ISBN

1-4244-0190-9

Type

conf

DOI

10.1109/ICPADM.2006.284193

Filename

4062682