Robust Distributed State Estimation in Power Systems: A Multi-Estimator Data Fusion Approach to Counteract Cyber-Attacks

Cyber security in power systems has become increasingly critical with the rise of network attacks such as Denial-of-Service (DoS) attacks and False Data Injection (FDI) attacks. These threats can severely compromise the integrity and reliability of state estimation, which are fundamental to the operation and control of power systems. In this manuscript, an estimation algorithm based on the fusion of information from multiple estimators is proposed to ensure that state estimation at critical buses can function properly in case of attacks. Our approach leverages a network of estimators that can dynamically adjust to maintain system stability and accuracy. Furthermore, a new detector is adopted based on Kullback-Leibler divergence to detect linear FDI attacks. To address stealthy attacks that may evade detection, we propose a novel weighting scheme that reduces the impact of attacks on estimation results. Numerical experiments demonstrate the effectiveness and accuracy of our proposed estimation algorithm under cyber attacks.