Distributed Robust State Estimation for Islanded Microgrids With Randomly Occurring Measurement Outliers

This article investigates state estimation in islanded microgrids (ImGs) over peer-to-peer sensor networks, focusing on scenarios involving unknown noise characteristics and measurement data corrupted by random outliers. To address this issue, a novel distributed robust state estimation algorithm is introduced. First, the inverse Wishart distribution serves as the prior distribution for estimating the unknown noise covariance matrix. The prediction probability density function (PDF) adopts a Gaussian framework, while the measurement PDF is characterized by a Gaussian–Student’s t mixture model. Furthermore, a variational Bayesian methodology is implemented to concurrently compute posterior estimations for both the state vector and associated model parameters. Besides, the alternating direction method of multipliers is utilized to achieve consensus among sensor nodes based on estimation results, while reducing communication bandwidth requirements. Finally, simulation experiments on an ImG with two distributed generation units demonstrate the effectiveness of the proposed algorithm through comparative simulations with existing methods.