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  • Statistical learning techniques and their applications for condition assessment of power transformer

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    Author(s)
    Ma, Hui
    Saha, Tapan
    Ekanayake, Chandima
    Griffith University Author(s)
    Ekanayake, Chandima MB.
    Year published
    2012
    Metadata
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    Abstract
    The condition of power transformers has a significant impact on the reliable operation of the electric power grid. A number of techniques have been in use for condition assessment of transformers. However, interpreting measurement data obtained from these techniques is still a non-trivial task; correlating measurement data to transformer condition is even more difficult. This paper investigates statistical learning techniques, which is able to learn statistical properties of a system from known samples and to predict the system output for unknown samples. Within the statistical learning framework, this paper develops a support ...
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    The condition of power transformers has a significant impact on the reliable operation of the electric power grid. A number of techniques have been in use for condition assessment of transformers. However, interpreting measurement data obtained from these techniques is still a non-trivial task; correlating measurement data to transformer condition is even more difficult. This paper investigates statistical learning techniques, which is able to learn statistical properties of a system from known samples and to predict the system output for unknown samples. Within the statistical learning framework, this paper develops a support vector machine (SVM) algorithm, which can be utilised for automatically analyzing measurement data and assessing condition of transformers. Case studies are presented to demonstrate the applicability of the developed algorithm for condition assessment of power transformer.
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    Journal Title
    IEEE Transactions on Dielectrics and Electrical Insulation
    Volume
    19
    Issue
    2
    DOI
    https://doi.org/10.1109/TDEI.2012.6180241
    Copyright Statement
    © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Electrical and Electronic Engineering not elsewhere classified
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/173339
    Collection
    • Journal articles

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