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Monitoring of low-voltage grids using artificial neural net..:

, In: 6th E-Mobility Power System Integration Symposium (EMOB 2022),

Liu, Z. ; Ringelstein, J. ; Ernst, M.... - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.2721

RT 
T1 <cTitle>6th E-Mobility Power System Integration Symposium (EMOB 2022)</cTitle>: T1 <Title>Monitoring of low-voltage grids using artificial neural networks and its field test application based on the beeDIP-platform</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10049824&Exemplar=1&LAN=DE
				
A1 Liu, Z.
A1 Ringelstein, J.
A1 Ernst, M.
A1 Requardt, B.
A1 Zauner, E.
A1 Baumbush, K.
A1 Wende-von Berg, S.
A1 Braun, M.
YR 2022
K1 distributed power generation
K1 distribution networks
K1 electric vehicles
K1 neural nets
K1 power engineering computing
K1 power generation economics
K1 power generation reliability
K1 power grids
K1 power system measurement
K1 power system state estimation
K1 ANN-based method
K1 artificial neural networks
K1 beeDIP-platform
K1 cost-effective operation
K1 distributed generators
K1 distribution system operators
K1 field test application
K1 German suburban grid
K1 grid monitoring methods
K1 grid state estimation method
K1 medium-voltage grid monitoring
K1 reliable measurement
K1 SimBench LV grids
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.2721
DO https://doi.org/10.1049/icp.2022.2721
SF ELIB - SuUB Bremen

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