Predictive learning and information fusion for condition assessment of power transformer

Ma, Hui, Saha, Tapan K. and Ekanayake, Chandima (2011). Predictive learning and information fusion for condition assessment of power transformer. In: 2011 IEEE Power and Energy Society General Meeting. 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, United States, (1-8). 24-29 July 2011. doi:10.1109/PES.2011.6039069

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Author Ma, Hui
Saha, Tapan K.
Ekanayake, Chandima
Title of paper Predictive learning and information fusion for condition assessment of power transformer
Conference name 2011 IEEE Power and Energy Society General Meeting
Conference location Detroit, MI, United States
Conference dates 24-29 July 2011
Proceedings title 2011 IEEE Power and Energy Society General Meeting
Journal name IEEE Power and Energy Society General Meeting
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/PES.2011.6039069
ISBN 9781457710001
9781457710018
ISSN 1944-9925
Start page 1
End page 8
Total pages 8
Language eng
Abstract/Summary To ensure the reliable operation of the power transformer, its conditions must be continuously monitored and assessed. The transformer condition assessment should make use every piece of information (evidence), which includes not only the measurement data of the transformer under investigation, but also the historic data of this transformer and other similar transformers. To acquire an integrated “picture” of transformer health conditions, one needs to combine the diagnosis results obtained from field measurements, laboratory tests, expert experience, utilities practices, and industry standards. This paper applies predictive learning and information fusion techniques for condition assessment of transformer. The predictive learning explores statistical properties from historic data and makes assessment of the property on the transformers. The information fusion integrates various evidences obtained from different sources. This paper develops several predictive learning and information fusion algorithms. Case studies are presented in this paper.
Keyword Condition monitoring
Dissolved gas analysis
Information fusion
Polarization/depolarization currents
Power transformer
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Tue, 20 Dec 2011, 23:49:23 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering