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

    A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis

    Access Status
    Fulltext not available
    Authors
    Abu-Siada, Ahmed
    Hmood, S
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Abu-Siada, A. and Hmood, S. 2015. A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis. Electrical Power and Energy Systems. 67: pp. 401-408.
    Source Title
    Electrical Power and Energy Systems
    DOI
    10.1016/j.ijepes.2014.12.017
    ISSN
    0142-0615
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/13790
    Collection
    • Curtin Research Publications
    Abstract

    Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers.

    Related items

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

    • Standardization of DGA interpretation techniques using fuzzy logic approach
      Hmood, S.; Abu-Siada, Ahmed; Masoum, Mohammad Sherkat; Islam, Syed (2012)
      Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely ...
    • A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis
      Abu Siada, Ahmed; Hmood, S.; Islam, Syed (2013)
      Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on ...
    • Fuzzy logic approach for power transformer asset management based on dissolved gas-in-oil analysis
      Abu-Siada, Ahmed; Hmood, S. (2013)
      Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools which can be facilitated to determine transformer criticality ranking and hence identifying a ...
    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.