Abstract
The dynamic non-linear state-space model of a power-system consisting of synchronous generators, buses, and static loads has been linearized and a linear measurement function has been considered. A distributed dynamic framework for estimating the state vector of the power system has been designed here. This framework employs a type of distributed Kalman filter (DKF) known as a Kalman consensus filter (KCF) which is located at distributed control centers (DCCs) that fuse locally available noise ridden measurements, state vector estimates of neighboring control centers, and a prediction obtained by the linearized model to obtain a filtered state vector estimate. Further, the local residual at each control center is checked by a median χ2detector designed here for bad data/Gaussian attack detection. Simulation results show the working of the KCF for an 8 bus 5 generator system, and the efficacy of the median χ2 detector in detecting the DCC affected by Gaussian attacks.
Recommended Citation
A. Fernandes et al., "Detection of Gaussian Attacks in Power Systems under a Scalable Kalman Consensus Filter Framework," IEEE Power and Energy Society General Meeting, Institute of Electrical and Electronics Engineers, Jan 2021.
The definitive version is available at https://doi.org/10.1109/PESGM46819.2021.9638022
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
attack mitigation; Distributed estimation; security
International Standard Book Number (ISBN)
978-166540507-2
International Standard Serial Number (ISSN)
1944-9933; 1944-9925
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2021
Comments
National Science Foundation, Grant GNT 1837472