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AuthorShaban, Khaled Bashir
AuthorEl-Hag, Ayman H.
AuthorBenhmed, Kamel
Available date2021-07-05T11:03:42Z
Publication Date2016
Publication NameIEEE Transactions on Power Delivery
ResourceScopus
URIhttp://dx.doi.org/10.1109/TPWRD.2016.2521320
URIhttp://hdl.handle.net/10576/21190
AbstractIn this letter, the ranges of furan content in oil in power transformers are predicted using measurements of oil tests, such as breakdown voltage, acidity, water content, and dissolved gas analysis. Predictive models based on machine-learning techniques are trained and tested to estimate the furan level. A prediction accuracy of 90% is achieved when using k-nearest neighbors as the classification model with a wrapper method as the feature selection technique. 2016 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectArtificial intelligent
furan
health index
transformer
TitlePrediction of Transformer Furan Levels
TypeArticle
Pagination1778-1779
Issue Number4
Volume Number31


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