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...

Full description

Saved in:

Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 25; no. 3; p. 943
Main Authors:	Zhao, Siliang, Luo, Wuman, Shu, Qin, Xu, Fangwei
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 05.02.2025 MDPI
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!