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Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the chi-squared-test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper, using synthetic PMU measurements and intelligently designed FDI attacks, it is shown that if an attack is suddenly injected into the system, a predictive filter with sufficient accuracy is able to detect it. However, an attacker can gradually increase the magnitude of the attack to avoid detection, and still cause damage to the system.
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Detection of Gaussian Attacks in Power Systems under a Scalable Kalman Consensus Filter Framework
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 chi-squared detector 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 chi-squared detector in detecting the DCC affected by Gaussian attacks.
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- Award ID(s):
- 1837472
- PAR ID:
- 10299541
- Date Published:
- Journal Name:
- 2021 IEEE PES General Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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