Design and implementation of a power quality disturbance classifier: An AI approach

M. B. I. Reaz*, F. Choong, M. S. Sulaiman, F. Mohd-Yasin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


This paper presents a new intelligent system incorporating wavelet transform, artificial neural network and fuzzy logic to automate the classification of power quality disturbance. This novel and efficient method in hardware, based on FPGA technology showed improved performance over existing approaches for power quality disturbance detection and classification on six types of disturbances including sag, swell, transient, fluctuation, interruption and normal waveform. The approach obtained an average classification accuracy of 98.19%. The design was successfully implemented, tested and validated on Altera APEX EP20K200EBC652-1X FPGA utilizing 1209 logic cells and achieved a maximum frequency of 263.71 MHz.

Original languageEnglish
Pages (from-to)623-631
Number of pages9
JournalJournal of Intelligent and Fuzzy Systems
Issue number6
Publication statusPublished - 2006


  • Artificial neural network
  • Field programmable gate array
  • Fuzzy logic
  • Power quality
  • Wavelet transform

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence


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