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Characterization of cellulose bridging pattern in transformer oil using feature extraction technique

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dc.contributor.authorMustafa N.B.A.en_US
dc.contributor.authorRamasamy I.D.en_US
dc.contributor.authorNordin F.H.en_US
dc.contributor.authorAli N.H.N.en_US
dc.contributor.authorZainuddin H.en_US
dc.contributor.authorDaud M.M.en_US
dc.contributor.authorid57191952020en_US
dc.contributor.authorid58069129200en_US
dc.contributor.authorid25930510500en_US
dc.contributor.authorid58069058300en_US
dc.contributor.authorid26423386300en_US
dc.contributor.authorid57205596095en_US
dc.date.accessioned2023-05-29T09:38:53Z
dc.date.available2023-05-29T09:38:53Z
dc.date.issued2022
dc.descriptionCellulose; Deterioration; Extraction; Insulating oil; Insulation; Oil filled transformers; Textures; Cellulose bridging; Early prediction; Electrical insulating properties; Feature descriptors; Feature extraction techniques; Features extraction; Highest temperature; Image processing tools; Shape features; Texture features; Feature extraction; Arching; Cellulose; Extraction; Formation; Images; Index Terms; Insulating Oil; Thicknessen_US
dc.description.abstractTransformer oil or insulating oil is stable at high temperatures and has excellent electrical insulating properties. The most frequent problems occurred in transformer were related to the defects and weakness of the insulation systems. Many diagnostic methods have been introduced to provide the reliable assessment of insulating oil quality. Hence, in this work, image processing technique known as feature extraction is used to measure the cellulose bridging thickness in pre-bridging and bridging stage. These two stages were considered as early prediction before breakdown occurs. The cellulose bridging formation recorded in three selected types of transformer oils which include MIDEL 7131, PFAE and Gemini X mineral oil. The cellulose bridging images were captured from the bridging formation videos and 60 images were chosen from the extracted cellulose bridging images for each transformer oil. The 60 images were divided equally for pre-bridging stage and bridging stage. The captured images were then fed into feature extraction process to extract eight feature descriptors which include area, minor-axis length, major-axis length, orientation, contrast, correlation, energy and homogeneity. In our findings, the pixel values increase proportionally with the cellulose bridging thickness. Hence, distinct pattern of pre-bridging and bridging stages were illustrated in all feature descriptors. With this technique, an early prediction can be made to analyze the deterioration of transformer oil. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/PECon54459.2022.9987705
dc.identifier.epage224
dc.identifier.scopus2-s2.0-85146418956
dc.identifier.spage219
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85146418956&doi=10.1109%2fPECon54459.2022.9987705&partnerID=40&md5=5964ced54547e702f13c2b78418e08c9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27035
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2022 IEEE International Conference on Power and Energy: Advancement in Power and Energy Systems towards Sustainable and Resilient Energy Supply, PECon 2022
dc.titleCharacterization of cellulose bridging pattern in transformer oil using feature extraction techniqueen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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