Design of robust transformer fault diagnosis system using evolutionary fuzzy logic

Yann Chang Huang, Hong Tzer Yang, Ching Lien Huang

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to posses superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

Original languageEnglish
Pages (from-to)613-616
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Publication statusPublished - 1996 Jan 1
EventProceedings of the 1996 IEEE International Symposium on Circuits and Systems, ISCAS. Part 1 (of 4) - Atlanta, GA, USA
Duration: 1996 May 121996 May 15

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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