Intelligent decision support for diagnosis of incipient transformer faults using self-Organizing Polynomial Networks

Hong-Tzer Yang, Yann Chang Huang

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

To serve as an intelligent decision support for the transformer fault diagnosis, a new self-organizing polynomial networks (SOPNs) modeling technique is proposed and implemented in this paper. The technique heuristically formulates the modeling problem into a hierarchical architecture with several layers of functional nodes of simple low-order polynomials. The networks handle the numerical, complicated, and uncertain relationships of dissolved gas contents of the transformers to fault conditions. Verification of the proposed approach has been accomplished through a number of experiments using practical numerical diagnostic records of the transformers of Taiwan power (Taipower) systems. In comparison to the results obtained from the conventional dissolved gas analysis (DGA) and the artificial neural networks (ANNs) classification methods, the proposed method has been shown to possess far superior performances both in developing the diagnosis system and in identifying the practical transformer fault cases.

Original languageEnglish
Pages (from-to)946-952
Number of pages7
JournalIEEE Transactions on Power Systems
Volume13
Issue number3
DOIs
Publication statusPublished - 1998 Dec 1

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Polynomials
Gas fuel analysis
Failure analysis
Neural networks
Gases
Experiments

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

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Intelligent decision support for diagnosis of incipient transformer faults using self-Organizing Polynomial Networks. / Yang, Hong-Tzer; Huang, Yann Chang.

In: IEEE Transactions on Power Systems, Vol. 13, No. 3, 01.12.1998, p. 946-952.

Research output: Contribution to journalArticle

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