VHDL Modeling for Classification of Power Quality Disturbance Employing Wavelet Transform, Artificial Neural Network and Fuzzy Logic

Mamun Bin Ibne Reaz, F. Choong, F. Mohd-Yasin

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

The identification and classification of voltage and current disturbances are important tasks in the monitoring and protection of power systems. Most power quality disturbances are non-stationary and transitory and both detection and classification have proved to be very demanding. New intelligent system technologies that use wavelet transforms, expert systems and artificial neural networks include some unique advantages regarding fault analysis. This paper presents a new approach to classifying six classes of signals: Five types of disturbance including sag, swell, transient, fluctuation, interruption, and the normal waveform. The concept of discrete wavelet transform for feature extraction from the power disturbance signal, combined with an artificial neural network and incorporating fuzzy logic to offer a powerful tool for detecting and classifying power quality problems, is introduced. The system was modeled using VHDL, a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. The extensive simulation results confirm the feasibility of the proposed algorithm. This method proposed herein classified and obtained 98.19% classification accuracy from the application of this system to software-generated signals and utility sampled disturbance events.

Original languageEnglish
Pages (from-to)867-881
Number of pages15
JournalSIMULATION
Volume82
Issue number12
DOIs
Publication statusPublished - 1 Dec 2006

Keywords

  • artificial neural network
  • fuzzy logic
  • modeling
  • Power quality
  • VHDL
  • wavelet transform

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design

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