Controllable Blind AC FDIA via Physics-Informed Extrapolative AVAE

False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectively control state estimation errors and are confin...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 25; číslo 3; s. 943
Hlavní autori:	Zhao, Siliang, Luo, Wuman, Shu, Qin, Xu, Fangwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 05.02.2025 MDPI
Predmet:	AC state estimation Adaptability controllable false data injection attack data driven Electric power systems extrapolative adversarial variational autoencoder Iran Knowledge Laws, regulations and rules Machine learning Methods Parameter estimation Physics physics informed Iran AC state estimation data driven extrapolative adversarial variational autoencoder controllable false data injection attack physics informed
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
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