Partial discharge pattern recognition of cast-resin current transformers using fuzzy C-means clustering approach

Wen Yeau Chang, Hong-Tzer Yang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Partial discharge (PD) measurement and recognition is a significant tool for potential failure diagnosis of the high-voltage equipment. This paper proposes the application of fuzzy c-means (FCM) clustering approach to recognize partial discharge patterns of cast-resin current transformer (CRCT). The PD patterns are measured by using a commercial PD detector. A set of features, used as operators, for each PD pattern is extracted through statistical schemes. The significant features of PD patterns are extracted by using the nonlinear principal component analysis (NLPCA) method. The proposed FCM classifier has the advantages of high robustness and effectiveness to ambiguous patterns and is useful in recognizing the PD patterns of the high-voltage equipment. To verify the effectiveness of the proposed method, the classifier was verified on 250 sets of field-test PD patterns of CRCTs. The test results show that the proposed approach may achieve quite satisfactory recognition of PD patterns.

Original languageEnglish
Pages (from-to)172-181
Number of pages10
JournalWSEAS Transactions on Computer Research
Volume3
Issue number3
Publication statusPublished - 2008 Mar

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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