An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and l...

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Veröffentlicht in:	Electric power systems research Jg. 232; S. 110407
Hauptverfasser:	Zideh, Mehdi Jabbari, Khalghani, Mohammad Reza, Solanki, Sarika Khushalani
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.07.2024
Schlagworte:	Adversarial autoencoder Cyber attack detection False data injection attacks Generative adversarial networks Power distribution grids Unsupervised data-driven method Generative adversarial networks False data injection attacks Power distribution grids Cyber attack detection Unsupervised data-driven method Adversarial autoencoder
ISSN:	0378-7796, 1873-2046
Online-Zugang:	Volltext
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