Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/653
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dc.contributor.authorIslam, M-
dc.contributor.authorLee, G-
dc.contributor.authorHettiwatte, S. N-
dc.contributor.authorWilliams, K-
dc.date.accessioned2022-01-13T05:59:51Z-
dc.date.available2022-01-13T05:59:51Z-
dc.date.issued2017-11-07-
dc.identifier.citationCited by 41en_US
dc.identifier.issn0885-8977-
dc.identifier.urihttp://localhost:80/handle/123456789/653-
dc.description.abstractA power transformer is one of the most crucial items of equipment in the electricity supply chain. The reliability of this valuable asset is strongly dependent on the condition of its subsystems such as insulation, core, windings, bushings and tap changer. Integration of various measured parameters of these subsystems makes it possible to evaluate the overall health condition of an in-service transformer. This paper develops an artificially intelligent algorithm based on multiple general regression neural networks to combine the operating condition of various subsystems of a transformer to form a quantitative health index. The model is developed using a training set derived from four conditional boundaries based on IEEE standards, the literature and the knowledge of transformer experts. Performance of the proposed method is compared with expert classifications using a database of 345 power transformers. This shows that the proposed method is reliable and effective for condition assessment and is sensitive to poor condition of any single subsystem.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Transactions on Power Delivery;Vol 33 Issue 4 Pages 1903-1912-
dc.subjectArtificial intelligenceen_US
dc.subjectgeneral regression neural network (GRNN)en_US
dc.subjecthealth indexen_US
dc.subjectpower transformeren_US
dc.subjectsubsystemsen_US
dc.titleCalculating a health index for power transformers using a subsystem-based GRNN approachen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TPWRD.2017.2770166en_US
Appears in Collections:Research Papers
Research Papers - Department of Electrical and Electronic Engineering
Research Papers - SLIIT Staff Publications

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