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  • Predictive learning and information fusion for condition assessment of power transformer

    Author(s)
    Ma, Hui
    Saha, Tapan Kumar
    Ekanayake, Chandima
    Griffith University Author(s)
    Ekanayake, Chandima MB.
    Year published
    2011
    Metadata
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    Abstract
    To ensure the reliable operation of the power transformer, its conditions must be continuously monitored and assessed. The transformer condition assessment should make use every piece of information (evidence), which includes not only the measurement data of the transformer under investigation, but also the historic data of this transformer and other similar transformers. To acquire an integrated “picture” of transformer health conditions, one needs to combine the diagnosis results obtained from field measurements, laboratory tests, expert experience, utilities practices, and industry standards. This paper applies predictive ...
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    To ensure the reliable operation of the power transformer, its conditions must be continuously monitored and assessed. The transformer condition assessment should make use every piece of information (evidence), which includes not only the measurement data of the transformer under investigation, but also the historic data of this transformer and other similar transformers. To acquire an integrated “picture” of transformer health conditions, one needs to combine the diagnosis results obtained from field measurements, laboratory tests, expert experience, utilities practices, and industry standards. This paper applies predictive learning and information fusion techniques for condition assessment of transformer. The predictive learning explores statistical properties from historic data and makes assessment of the property on the transformers. The information fusion integrates various evidences obtained from different sources. This paper develops several predictive learning and information fusion algorithms. Case studies are presented in this paper.
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    Conference Title
    2011 IEEE Power and Energy Society General Meeting
    DOI
    https://doi.org/10.1109/PES.2011.6039069
    Subject
    Electrical and Electronic Engineering not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/173611
    Collection
    • Conference outputs

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