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    Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation

    Access Status
    Fulltext not available
    Authors
    Zhao, Z.
    Yao, C.
    Li, C.
    Islam, Syed
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhao, Z. and Yao, C. and Li, C. and Islam, S. 2017. Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation. IEEE Transactions on Industrial Electronics. 65 (4): pp. 3509 - 3519.
    Source Title
    IEEE Transactions on Industrial Electronics
    DOI
    10.1109/TIE.2017.2752135
    ISSN
    0278-0046
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/59587
    Collection
    • Curtin Research Publications
    Abstract

    IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types.

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