Partial Discharge Classification with Transformer Neural Networks

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dc.contributor.authorCheypoca, Thepjit
dc.contributor.authorPromphanich, Wiboon
dc.contributor.authorThway, Aung Ye
dc.contributor.authorHankae, Angelina Phimpissada
dc.contributor.authorJeenmuang, Siwakorn
dc.contributor.authorPattanadech, Norasage
dc.date.accessioned2026-08-06T10:43:35Z
dc.date.available2026-08-06T10:43:35Z
dc.date.issued2024-01-01
dc.description.abstractThis paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed
dc.identifier.citationProceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials, 93-96, 2024
dc.identifier.doi10.1109/ICPADM61663.2024.10750584
dc.identifier.other2-s2.0-85211084620
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14980
dc.sourceProceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials
dc.subjectClassification
dc.subjectclassification
dc.subjectCurse of Dimensionality
dc.subjectpartial discharge (PD)
dc.subjectTransformer Neural Networks
dc.titlePartial Discharge Classification with Transformer Neural Networks
dc.typeConference Paper

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