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AuthorGhunem, Refat Atef
AuthorShaban, Khaled Bashir
AuthorEl-Hag, Ayman Hassan
AuthorAssaleh, Khaled
Available date2022-12-21T10:01:48Z
Publication Date2012
Publication Name2012 IEEE Electrical Power and Energy Conference, EPEC 2012
ResourceScopus
URIhttp://dx.doi.org/10.1109/EPEC.2012.6474933
URIhttp://hdl.handle.net/10576/37530
AbstractInsulation resistance (IR) or Megger test has been commonly performed in both preventive and corrective maintenance activities to verify power transformers' insulation condition. Other insulation diagnosis tests such as oil breakdown voltage (BDV), water content and dissolved-gas-in-oil analysis have been conducted along with the IR test. In this paper, a prediction model is developed to correlate IR measurements of the power transformer with its oil quality parameters, the concentration of its total dissolved combustible gases (TDCG), and its carbon dioxide to carbon monoxide concentration (CO 2/CO) ratio. Four models, based on feed-forward artificial neural networks with back-propagation, are trained on collected data of real measurements. Accuracy levels of 96%, 84%, 88%, and 91% are obtained for BDV, water content, TDCG, and CO2/CO ratio respectively. Utilizing the proposed model can reduce maintenance costs by preventing and shortening transformers' outage times using inexpensive test, i.e. using IR test only. 2012 IEEE.
Languageen
Subjectartificial neural network (ANN)
asset management
dissolved-gas-in-oil analysis (DGA)
preventive and corrective transformer maintenance
TitleTowards cost-effective maintenance of power transformer by accurately predicting its insulation condition
TypeConference Paper
Pagination111-116
dc.accessType Abstract Only


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