Open AccessA Convolutional Neural Network-based UHF Partial Discharge Atlas Classification System for GIS
Author(s) -
Congcong Zhang,
Gang Wang,
Dong Gao,
Wei Yin,
Kai Wang,
Ming Lu,
Yingnan Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/3/032086
Subject(s) - partial discharge , convolutional neural network , convolution (computer science) , computer science , ultra high frequency , pattern recognition (psychology) , artificial intelligence , layer (electronics) , artificial neural network , engineering , telecommunications , voltage , materials science , electrical engineering , composite material
In order to solve the difficult problem of partial discharge pattern recognition caused by large amount of partial discharge detection data and complex multi-source, a partial discharge pattern recognition algorithm based on VGG-16 convolution neural network is proposed. The parameters of VGG-16 network model are optimized in convolution layer, pool layer and connection layer by means of migration learning. The VGG-16 model is superior to LeNet-5 model and has higher recognition accuracy.