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  • Smart Transformer for Smart Grid - Intelligent Framework and Techniques for Power Transformer Asset Management

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    Author(s)
    Ma, Hui
    Saha, Tapan
    Ekanayake, Chandima
    Martin, Daniel
    Griffith University Author(s)
    Ekanayake, Chandima MB.
    Year published
    2015
    Metadata
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    Abstract
    Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, ...
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    Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, extracting representative characteristics from raw data, and identifying types of faults in transformers. This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management. The hardware and software platform for implementing the proposed intelligent framework will also be presented in this paper.
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    Journal Title
    IEEE Transactions on Smart Grid
    Volume
    6
    Issue
    2
    DOI
    https://doi.org/10.1109/TSG.2014.2384501
    Copyright Statement
    © 2015 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
    Interdisciplinary Engineering
    Publication URI
    http://hdl.handle.net/10072/173022
    Collection
    • Journal articles

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