Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks

    Access Status
    Fulltext not available
    Authors
    Masoum, Mohammad
    Jamali, S.
    Ghaffarzadeh, N.
    Date
    2010
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Masoum, M.A.S. and Jamali, S. and Ghaffarzadeh, N. 2010. Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks. IET Science, Measurement and Technology. 4 (4): pp. 193-205.
    Source Title
    IET Science, Measurement and Technology
    DOI
    10.1049/iet-smt.2009.0006
    ISSN
    17518822
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/17635
    Collection
    • Curtin Research Publications
    Abstract

    A novel approach for detection and classification of power quality (PQ) disturbances is proposed. The distorted waveforms (PQ events) are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio. The DWT is also used to decompose the signal of PQ events and to extract its useful information. Proper feature vectors are selected and applied in training the wavelet network classifier. The effectiveness of the proposed method is tested using a wide spectrum of PQ disturbances including dc offset, harmonics, flicker, interrupt, sag, swell, notching, transient and combinations of these events. Comparison of test results with those generated by other existing methods shows enhanced performance with a classification accuracy of 98.18%. The main contribution of the paper is an accurate (because of proper selection of feature vectors), fast (e.g. a new de-noising approach with proposed identification criterion) and robust (at different signal-to-noise ratios) wavelet network-based algorithm (as compared to the conventional wavelet-based algorithms) for detection/classification of individual, as well as combined PQ disturbances.

    Related items

    Showing items related by title, author, creator and subject.

    • Reducing the dimensionality of hyperspectral remotely sensed data with applications for maximum likelihood image classification
      Santich, Norman Ty (2007)
      As well as the many benefits associated with the evolution of multispectral sensors into hyperspectral sensors there is also a considerable increase in storage space and the computational load to process the data. ...
    • Advanced satellite radar interferometry for small-scale surface deformation detection
      Baran, Ireneusz (2004)
      Synthetic aperture radar interferometry (InSAR) is a technique that enables generation of Digital Elevation Models (DEMs) and detection of surface motion at the centimetre level using radar signals transmitted from a ...
    • Audio networks for speech enhancement and indexing
      Kühnapfel, Thorsten (2009)
      For humans, hearing is the second most important sense, after sight. Therefore, acoustic information greatly contributes to observing and analysing an area of interest. For this reason combining audio and video cues for ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.