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

Md. Mamun Ibne Reaz, F. Choong, M. S. Sulaiman, F. Mohd-Yasin

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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
Volume17
Issue number6
Publication statusPublished - 2006
Externally publishedYes

Fingerprint

Power Quality
Power quality
Classifiers
Disturbance
Classifier
Field Programmable Gate Array
Field programmable gate arrays (FPGA)
Intelligent systems
Intelligent Systems
Waveform
Wavelet transforms
Wavelet Transform
Fuzzy Logic
Fuzzy logic
Artificial Neural Network
Hardware
Logic
Fluctuations
Neural networks
Cell

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence

Cite this

Design and implementation of a power quality disturbance classifier : An AI approach. / Ibne Reaz, Md. Mamun; Choong, F.; Sulaiman, M. S.; Mohd-Yasin, F.

In: Journal of Intelligent and Fuzzy Systems, Vol. 17, No. 6, 2006, p. 623-631.

Research output: Contribution to journalArticle

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