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AuthorBenhmed, Kamel
AuthorMooman, Abdelniser
AuthorYounes, Abdunnaser
AuthorShaban, Khaled
AuthorEl-Hag, Ayman
Available date2022-12-21T10:01:47Z
Publication Date2018
Publication NameIEEE Transactions on Power Delivery
ResourceScopus
URIhttp://dx.doi.org/10.1109/TPWRD.2017.2762920
URIhttp://hdl.handle.net/10576/37506
AbstractThis letter investigates an approach based on feature selection and classification techniques to reduce assessment complexities of power transformers. This approach decreases the number of features by extracting the most influential ones when determining the transformers health index (HI). Several filters and wrapper-based feature selection methods are investigated. The effectiveness of the selected features is validated through performance evaluations of various classification models. The experimental results demonstrate that water content, acidity, breakdown voltage, and FFA (Furan), are the most influential testing parameters in determining the transformer HI. 1986-2012 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectArtificial intelligence
condition monitoring
transformer
TitleFeature selection for effective health index diagnoses of power transformers
TypeArticle
Pagination3223-3226
Issue Number6
Volume Number33
dc.accessType Abstract Only


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