Secure Distributed State Estimation for Microgrids With Eavesdroppers Based on Variable Decomposition

Secure state estimation is becoming more popular due to the inherent vulnerabilities of communication networks in essence, which could give rise to potential data leakage and manipulation of microgrids. The paper addresses the issue of secure distributed state estimation for a class of microgrids with potential outliers occurring in sensor measurements. First, a secure distributed estimator is constructed by introducing both an artificial saturation rule to achieve outlier resilience and a variable decomposition strategy to safeguard data security, where the generated dynamic key is a time-varying sequence satisfying the predetermined constraint. Deep variance analysis is carried out to profoundly disclose the relationship between private and public estimation error covariance, in accordance with the employed decomposition rule. An upper bound of error covariance is determined by two sets of recursive matrix equations in contrast to that of traditional distributed estimation. Furthermore, the desired estimator gains are obtained recursively with the aid of optimizing the upper bound obtained above. In the end, a simulation example is proposed to confirm the effectiveness and security of the proposed algorithm.