Power quality disturbance classification based on adaptive neuro-fuzzy system

Thai Nguyen, Yuan Liao

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


This paper presents an adaptive neuro-fuzzy inference system and a set of novel features for classification of power quality disturbances. The most common types of disturbances including flickers, harmonics, impulses, notches, outages, sags, swells, and switching transients are considered in this research. The proposed method employs voltage waveforms for analysis. The features are extracted utilizing the signal processing techniques such as the windowed discrete Fourier transform and S-transform. Evaluation studies based on both simulated and field data are reported.

Original languageEnglish
Article number4
JournalInternational Journal of Emerging Electric Power Systems
Issue number3
StatePublished - 2009


  • Adaptive neuro-fuzzy inference system
  • Decision making
  • Feature extraction
  • Power quality disturbances
  • S-transform

ASJC Scopus subject areas

  • Energy Engineering and Power Technology


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