Fuzzy learning vector quantization networks for power transformer condition assessment

Hong Tzer Yang, Chiung Chou Liao, Jeng Hong Chou

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)


To improve the assessment capability of power transformers, this paper proposes a new intelligent decision support system based on fuzzy learning vector quantization (FLVQ) networks. In constructing the system, a fuzzy-based classifier is designed to divide the historical data for dissolved gas analysis (DGA) into various categories with different levels of gas attributes. For each category of gas attributes, a learning vector quantization (LVQ) network is trained to be responsible for the classification of the potential faults due to insulation deterioration. The assessment approach has been tested on the DGA data from Taiwan Power Company (TPC) and compared with the previous fuzzy diagnosis system and the existing multi-layered back-propagation based artificial neural networks (BPANN) methods. Remarkable classification accuracy and far less training efforts of the proposed approach are achieved in this paper.

Original languageEnglish
Pages (from-to)143-149
Number of pages7
JournalIEEE Transactions on Dielectrics and Electrical Insulation
Issue number1
Publication statusPublished - 2001 Feb

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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