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dc.contributor.authorIslam, M. D. M-
dc.contributor.authorHettiwatte, S. N-
dc.contributor.authorLee, G-
dc.date.accessioned2022-01-13T08:41:00Z-
dc.date.available2022-01-13T08:41:00Z-
dc.date.issued2017-11-19-
dc.identifier.issn2474-1507-
dc.identifier.urihttp://localhost:80/handle/123456789/672-
dc.description.abstractMany industrial devices are monitored by measuring several attributes at a time. For electrical power transformers their condition can be monitored by measuring electrical characteristics such as frequency response and dissolved gas concentrations in insulating oil. These vectors can be processed to indicate the health of a transformer and predict its probability of failure. One weakness of this approach is that missing measurements render the vector incomplete and unusable. A solution is to estimate missing measurements using a General Regression Neural Network on the assumption that they are correlated with other measurements. If these missing values are completed, the entire vector of measurements can be used as an input to a pattern classifier. To test this approach, known values were deliberately omitted allowing an estimate to be compared with actual values. Tests show the method is able to accurately estimate missing values based on a finite set of complete observations.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2017 Australasian Universities Power Engineering Conference (AUPEC);Pages 1-5-
dc.subjectArtificial Intelligenceen_US
dc.subjectGeneral Regression Neural Network (GRNN)en_US
dc.subjectMissing Dataen_US
dc.subjectPower Transformeren_US
dc.titleMissing measurement estimation of power transformers using a GRNNen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/AUPEC.2017.8282431en_US
Appears in Collections:Research Papers
Research Papers - Department of Electrical and Electronic Engineering
Research Papers - SLIIT Staff Publications

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