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  • Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier

    Author(s)
    Reaz, MBI
    Choong, F
    Sulaiman, MS
    Mohd-Yasin, F
    Griffith University Author(s)
    Mohd-Yasin, Faisal
    Year published
    2007
    Metadata
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    Abstract
    Identification and classification of voltage and current disturbances in power systems are important tasks in their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power quality related events. This article focuses on the application of wavelet transform technique to extract features from power quality disturbance waveforms and their classification using a combination of artificial neural network and fuzzy logic. The disturbances of interest include sag, swell, transient, fluctuation and interruption ...
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    Identification and classification of voltage and current disturbances in power systems are important tasks in their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power quality related events. This article focuses on the application of wavelet transform technique to extract features from power quality disturbance waveforms and their classification using a combination of artificial neural network and fuzzy logic. The disturbances of interest include sag, swell, transient, fluctuation and interruption waveform. The system is modelled using VHDL and synthesized to Mercury EP1M120F484C5 FPGA, tested and validated. Comparisons, verification and analysis on disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.19%. This novel and efficient method, and also implementation of the method in hardware based on FPGA technology, showed improved performance over existing approaches for power quality disturbance detection and classification.
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    Journal Title
    Electric Power Components and Systems
    Volume
    35
    Issue
    1
    DOI
    https://doi.org/10.1080/15325000600815431
    Subject
    Electrical and Electronic Engineering not elsewhere classified
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/46716
    Collection
    • Journal articles

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