Pattern Recognition Techniques for Power Transformer Insulation Diagnosis - A Comparative Study Part 1: Framework, Literature, and Illustration
Article
Article Title | Pattern Recognition Techniques for Power Transformer Insulation Diagnosis - A Comparative Study Part 1: Framework, Literature, and Illustration |
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ERA Journal ID | 36582 |
Article Category | Article |
Authors | Cui, Yi (Author), Ma, Hui (Author) and Saha, Tapan (Author) |
Journal Title | International Transactions on Electrical Energy System |
Journal Citation | 25 (10), pp. 2247-2259 |
Number of Pages | 13 |
Year | 2014 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 1430-144X |
1546-3109 | |
2050-7038 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/etep.1959 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/etep.1959 |
Abstract | The condition of the insulation system of a power transformer has a significant impact on its overall reliability and serviceability. Transformer oil tests including breakdown voltage, acidity, dielectric dissipation factor, 2-furfuraldehyde, water content, and dissolved gases analysis have been commonly performed in utility companies to provide information regarding the conditions of transformer insulation. Over the past two decades, various pattern recognition techniques are proposed to interpret the oil tests results and make diagnosis on transformer insulation. However, there are still considerable challenging issues to be investigated before the pattern recognition technique can become a “ready-to-use” tool at utility companies. This paper provides a comparative study of pattern recognition techniques for power transformer insulation diagnosis using oil tests results. A general pattern recognition application framework will be outlined in the paper. And a comprehensive literature review on various pattern recognition techniques for transformer insulation diagnosis will be provided in the paper. The important issues for improving the applicability of pattern recognition techniques for transformer insulation diagnosis will also be discussed. A case study will be presented to demonstrate the procedure of applying pattern recognition techniques to practical transformer insulation diagnosis using oil test results. |
Keywords | dissolved gas analysis; insulation; oil characteristics; pattern recognition; power transformer |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
460308. Pattern recognition | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Queensland |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w48/pattern-recognition-techniques-for-power-transformer-insulation-diagnosis-a-comparative-study-part-1-framework-literature-and-illustration
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