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Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil

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dc.citedby1
dc.contributor.authorMustafa N.B.A.en_US
dc.contributor.authorNik Ali N.H.en_US
dc.contributor.authorZainuddin H.en_US
dc.contributor.authorDaud M.M.en_US
dc.contributor.authorNordin F.H.en_US
dc.contributor.authorid57191952020en_US
dc.contributor.authorid57196922007en_US
dc.contributor.authorid26423386300en_US
dc.contributor.authorid57205596095en_US
dc.contributor.authorid25930510500en_US
dc.date.accessioned2023-05-29T08:07:51Z
dc.date.available2023-05-29T08:07:51Z
dc.date.issued2020
dc.description.abstractTransformer is considered as one of the most important equipment in electrical power system networks. However, most problems occurred in transformer were related to the defects and weakness of the insulation systems. The oils used in transformer act as coolant and insulation purposes hence maintaining the dielectric strength of the transformer. In this work, electric field bridging pattern is observed from pre-breakdown and breakdown condition. The electric field bridging formation was recorded in the experimental setup and images were captured per frame. 193 images were randomly chosen from the whole video frames where 102 images were the pre-breakdown images and 91 images were the breakdown images. This system comprises of four stages: (i) a preprocessing stage to mark the electrodes tips and background subtraction; (ii) a segmentation stage to extract the electric field bridging formation in region of interest; (iii) a feature extraction stage to extract electric field bridging using feature descriptors, area, minor-axis and major-axis length (iv) a classification stage to identify the pre-breakdown and breakdown condition. System performance was evaluated using support vector machine (SVM), k-nearest neighbour (k-NN) and random forest (RF) and SVM provided the most promising accuracy that was 99%. The results show that the combination of three feature descriptors, area, minor-axis and major-axis length are the best features combination in identifying the transformer oil condition. In future work, further studies will be conducted to investigate the pattern of pre- and post-breakdown due to some similarity found in image pattern. Due to that, more feature descriptors will be identified to find a unique pattern between pre- and post-breakdown condition. Copyright � 2020 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v19.i3.pp1210-1218
dc.identifier.epage1218
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85085510486
dc.identifier.spage1210
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085510486&doi=10.11591%2fijeecs.v19.i3.pp1210-1218&partnerID=40&md5=3cb1043f7ea166de7461bb234819d6ee
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25284
dc.identifier.volume19
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleElectric field bridging pattern of pre-breakdown and breakdown condition in transformer oilen_US
dc.typeArticleen_US
dspace.entity.typePublication
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