Partial Discharge Classification With 1D Convolutional Neural Network

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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.accessioned2026-08-06T10:43:30Z
dc.date.available2026-08-06T10:43:30Z
dc.date.issued2024-01-01
dc.description.abstractThis paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal 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 Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 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, 89-92, 2024
dc.identifier.doi10.1109/ICPADM61663.2024.10750640
dc.identifier.other2-s2.0-85211156057
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14960
dc.sourceProceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials
dc.subject1D Convolutional Neural Network (1D CNN)
dc.subjectclassification
dc.subjectcurse of Dimensionality
dc.subjectpartial discharge (PD)
dc.titlePartial Discharge Classification With 1D Convolutional Neural Network
dc.typeConference Paper

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