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Distribution network fault identification method based on v..:

, In: Tsinghua-IET Electrical Engineering Academic Forum 2023,

Pan, S. ; Hu, J. ; Ma, H.... - p. None , 2023

Link: https://doi.org/10.1049/icp.2023.1829

RT 
T1 <cTitle>Tsinghua-IET Electrical Engineering Academic Forum 2023</cTitle>: T1 <Title>Distribution network fault identification method based on variational mode decomposition and semi-supervised learning</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10267872&Exemplar=1&LAN=DE
				
A1 Pan, S.
A1 Hu, J.
A1 Ma, H.
A1 Bi, R.
A1 Zhang, H.
A1 Wang, S.
A1 He, J.
A1 Yuan, Z.
A1 Liu, Y.
YR 2023
K1 convolutional neural nets
K1 deep learning (artificial intelligence)
K1 distribution networks
K1 fault diagnosis
K1 feature extraction
K1 pattern classification
K1 power distribution faults
K1 power engineering computing
K1 semi-supervised learning (artificial intelligence)
K1 AlexNet deep learning model
K1 characteristic matrix
K1 current signals
K1 distribution network fault identification method
K1 fault classification
K1 IEEE 33 nodes
K1 labeled feature matrix
K1 LPA semisupervised learning algorithm
K1 partial labels
K1 pixel matrix
K1 PSCAD
K1 three-phase voltage signal
K1 variational mode decomposition
K1 zero-sequence current time-frequency matrix
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2023.1829
DO https://doi.org/10.1049/icp.2023.1829
SF ELIB - SuUB Bremen

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