Transmission line fault type classification based on novel features and neuro-fuzzy system

Nguyen Thai, Liao Yuan

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


This article presents an adaptive neuro-fuzzy inference system and a set of novel features for the classification of transmission line fault types. The ten common types of faults, including line-to-ground faults, line-to-line faults, line-to-line-to-ground faults, and three-phase faults, are considered in this research. The proposed method employs only current waveforms, and the new features include correlation coefficients and inter-quartile ranges of current signals. For the decision-making system based on the neuro-fuzzy technique, two schemes have been investigatedone consisting of 128 rules and the other with 10 rules. Evaluation studies based on both electromagnetic transient program simulated data and field data have demonstrated very promising results for the proposed method.

Original languageEnglish
Pages (from-to)695-709
Number of pages15
JournalElectric Power Components and Systems
Issue number6
StatePublished - Apr 2010


  • Adaptive neuro-fuzzy inference system
  • Decision making
  • Fault type classification
  • Feature extraction

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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