Applying Deep Learning-based concepts for the detection of device misconfigurations in power systems

Research output: Contribution to journal › Article › peer-review

Original language	English
Number of pages	12
Journal	Sustainable Energy, Grids and Networks
Volume	32
DOIs	https://doi.org/10.1016/j.segan.2022.100851
Publication status	Published - 2022

Research Field

@article{d92cb8a1935e4557a89a4f3078c6bb4b,

title = "Applying Deep Learning-based concepts for the detection of device misconfigurations in power systems",

author = "David Fellner and Thomas Strasser and Wolfgang Kastner",

year = "2022",

doi = "10.1016/j.segan.2022.100851",

language = "English",

volume = "32",

journal = "Sustainable Energy, Grids and Networks",

issn = "2352-4677",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - Applying Deep Learning-based concepts for the detection of device misconfigurations in power systems

AU - Fellner, David

AU - Strasser, Thomas

AU - Kastner, Wolfgang

PY - 2022

Y1 - 2022

U2 - 10.1016/j.segan.2022.100851

DO - 10.1016/j.segan.2022.100851

M3 - Article

SN - 2352-4677

VL - 32

JO - Sustainable Energy, Grids and Networks

JF - Sustainable Energy, Grids and Networks

ER -