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Transformer mechanical integrity evaluation via unsupervised neural network (UNN) in smart grid network

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dc.contributor.authorBohari Z.H.en_US
dc.contributor.authorJali M.H.en_US
dc.contributor.authorBaharom M.F.en_US
dc.contributor.authorNasir M.N.M.en_US
dc.contributor.authorFariz N.M.en_US
dc.contributor.authorThayoob Y.H.M.en_US
dc.contributor.authorid56085889300en_US
dc.contributor.authorid56078350800en_US
dc.contributor.authorid56078250600en_US
dc.contributor.authorid55658799800en_US
dc.contributor.authorid57190289678en_US
dc.contributor.authorid6505876050en_US
dc.date.accessioned2023-05-29T06:11:48Z
dc.date.available2023-05-29T06:11:48Z
dc.date.issued2016
dc.descriptionBiomedical engineering; Computer networks; Condition monitoring; Conformal mapping; Control systems; Electric power transmission networks; Self organizing maps; Chinese Standard; Contingency analysis; Electrical systems; Integral part; Mechanical integrity; Smart grid; Smart grid networks; Unsupervised neural networks; Smart power gridsen_US
dc.description.abstractThis paper describes the classification of mechanical integrity of transformers using unsupervised neural networks (UNN). Transformers are the integral part of electrical system or smart grid networks since the last century. Self-Organizing Maps (SOM) is one type of UNN the widely used to do assessment on any system such as biomedical engineering, load contingency analysis and etc. The application of CIGRE standard and SOM in the research are enhancing the ability to do mechanical integrity assessment on the transformers for condition monitoring. Motivation for this research is to fill in the gap of excess FRA raw data for better assessment. This research proved that the new proposed method using SOM integrated with CIGRE standard able to do mechanical examination especially on core, winding and magnetic part of the transformer compared to current OMICRON SFRAnalyzer tool that employed Chinese Standard. � 2015 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7482178
dc.identifier.doi10.1109/ICCSCE.2015.7482178
dc.identifier.epage171
dc.identifier.scopus2-s2.0-84978908836
dc.identifier.spage167
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84978908836&doi=10.1109%2fICCSCE.2015.7482178&partnerID=40&md5=c60e5b80c9e816bb561a56d95a6d1807
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22718
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015
dc.titleTransformer mechanical integrity evaluation via unsupervised neural network (UNN) in smart grid networken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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