Pattern recognition techniques for power transformer insulation diagnosis: a comparative study. Part 2: implementation, case study,and statistical analysis

Cui, Yi, Ma, Hui and Saha, Tapan (2014) Pattern recognition techniques for power transformer insulation diagnosis: a comparative study. Part 2: implementation, case study,and statistical analysis. International Transactions on Electrical Energy Systems, 25 10: 2260-2274. doi:10.1002/etep.1963

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Cui, Yi
Ma, Hui
Saha, Tapan
Title Pattern recognition techniques for power transformer insulation diagnosis: a comparative study. Part 2: implementation, case study,and statistical analysis
Journal name International Transactions on Electrical Energy Systems   Check publisher's open access policy
ISSN 2050-7038
Publication date 2014-07-17
Year available 2014
Sub-type Article (original research)
DOI 10.1002/etep.1963
Open Access Status
Volume 25
Issue 10
Start page 2260
End page 2274
Total pages 15
Place of publication Chichester, West Sussex, United Kingdom
Publisher John Wiley & Sons
Collection year 2015
Language eng
Formatted abstract
Transformer oil tests such as breakdown voltage, resistivity, dielectric dissipation factor, water content, 2-furfuraldehyde, acidity, and different dissolved gasses have been adopted in utility companies for evaluating the conditions of transformer insulation. Over the past 20 years, various pattern recognition techniques have been applied for power transformer insulation diagnosis using oil tests results (oil characteristics). This paper investigates a variety of state-of-the-art pattern recognition algorithms for transformer insulation diagnosis. To verify the applicability and generalization capability of different pattern recognition algorithms, this paper implements 15 representative algorithms and conducts extensive case studies on eight oil characteristics datasets collected from different utility companies. A statistical performance (in terms of classification accuracy) comparison among different pattern recognition algorithms for transformer insulation diagnosis using oil characteristics is also conducted in the paper.
Keyword Dissolved gas analysis
Insulation
Oil characteristics
Pattern recognition
Power transformer
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article first published online: 17 JUL 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Thu, 04 Dec 2014, 13:48:15 EST by Dr Hui Ma on behalf of School of Information Technol and Elec Engineering