Trust-based distributed state estimation for microgrids with encoding-decoding mechanisms - Zhao, Peifeng; Liu, Hongjian; Huang, Tingwen; Tan, Hailong (Information sciences, 2023)
In this article, a trust-based estimation algorithm in a distributed paradigm is developed for microgrids whose information transmission obeys the predetermined encoding and decoding rule. Because of weak communication protection, and harsh electromagnetic or geographical environments, the output of sensors in microgrids could be subject to outliers or cyberattacks, which will lead to anomalous estimates. According to such a challenge, a simple clustering method is employed to distinguish between trusted and untrusted data. In light of distinguished data, a novel distributed estimator under an encoding and decoding scheme is then proposed where the correction of untrusted data is executed by the cluster information. Furthermore, a performable algorithm is developed by optimizing the upper bound of error covariance and a novel decomposition of system matrices, and a condition is derived to check the boundedness of the developed iterative algorithm. In the end, a simulation example is given to verify the effectiveness. •A simple clustering method is employed to distinguish trusted and untrusted data.•A novel distributed estimator is proposed where the correction of untrusted data is executed by the cluster information.•A performable algorithm is developed by optimizing the upper bound of error covariance.•A condition is derived to check the boundedness of the developed iterative algorithm.