State Estimation and Branch Current Learning Using Independent Local Kalman Filter With Virtual Disturbance Model

This paper presents a generalized approach to the design of independent local Kalman filters (KFs) without communication to be used for state estimation in distributed generation-based power systems. The design procedure is based on an improved model of the virtual disturbance concept proposed in a previous work. The local KFs are then synthesized based only on local models of the power network and on the characteristics of the associated virtual disturbance. The proposed solution is applied to an interconnected power network. By choosing appropriate models for the virtual disturbance, the local KFs can be suited for both dc and ac distribution systems. It is shown for both cases that the local KF can infer the local states of the network, including the aggregated branch currents coming from the other buses. Simulation results show improved results with respect to the previous proposed modeling approach even when the subsystems present widely different dynamics. The herein presented approach is well suited for the agent-based decentralized control of microgrids.