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    Power Quality Management and Classification for Smart Grid Application using Machine Learning

    Chiam DH 2023 Public.pdf (6.431Mb)
    Access Status
    Open access
    Authors
    Chiam, Dar Hung
    Date
    2023
    Supervisor
    Hann Lim
    Kah Haw Law
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Curtin Malaysia
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/92726
    Collection
    • Curtin Theses
    Abstract

    The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development.

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