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Partial Discharge Classification with Transformer Neural Networks
Author(s)
Promphanich, Wiboon
Thway, Aung Ye
Hankae, Angelina Phimpissada
Jeenmuang, Siwakorn
Date Issued
January 1, 2024
Type
Conference Paper
Abstract
This 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
Citation
Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials, 93-96, 2024