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Distributed Secure Consensus Estimation for Power Systems Against False Data Injection Attacks
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AI
DOI：10.1002/rnc.7936.png)
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Abstract
En 中文
As a result of the rapid growth of distributed state estimation in modern power systems, power networks are facing increasingly serious security problems, thus requiring the advancement of defense techniques against cyber attacks. This paper is devoted to investigating distributed consensus state estimation with a defense mechanism against false data injection (FDI) attacks for power systems. By introducing a transformation matrix, the local subsystem model associated with the mixed remote terminal unit and phasor measurement unit measurements is constructed. Taking into account historical estimation information on state variables without being attacked, a defense mechanism constructed by secure historical estimation value is presented to protect the estimator against FDI attacks while maintaining accuracy in estimation. Then a distributed Kalman consensus filter (DKCF) is hosted to estimate the dynamic states of power systems under FDI attack protector. Considering scalability in large-scale power systems, a suboptimal DKCF with the designed protector is developed. By means of the Lyapunov-based approach, a sufficient condition is provided to ensure that the proposed estimator equipped with the defense mechanism is stable. Finally, the proposed distributed state estimation algorithms are validated on an IEEE benchmark 14-bus power system.
Keywords:
defense mechanism
distributed Kalman consensus filter (DKCF)
false data injection (FDI) attack
power system state estimation
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