Design of Distributed State Estimation for Droop-Controlled Islanded Microgrids

This paper proposes a distributed algorithm for the dynamic state estimation of droop-controlled Islanded Microgrids (IMGs). In this regard, the IMG is clustered into a number of zones. Each zone has its own dynamic state estimation unit called Zone State Estimator (ZSE). Each ZSE updates the states of its zone using local measurements and exchange of information with adjacent zones. The proposed distributed state estimation approach adopts the particle filter state estimation technique in order to obtain the state estimation process in a distributed environment. The objective of the proposed design of distributed state estimation is to study the impacts of selecting the number and boundaries of zones on the performance of the proposed distributed state estimation algorithm. Case studies are simulated to evaluate the effectiveness of the proposed algorithm under different operating conditions. Monte Carlo simulation method was applied to verify the accuracy and robustness of the proposed distributed state estimator on tracking the ground truth.