A distributed MAP approach to dynamic state estimation with applications in power networks

This paper studies a state estimation problem for a networked dynamic system characterized by a communication graph. A new distributed state estimation method is based on a distributed MAP (maximum a posteriori) estimation algorithm for each node to update its local state. This distributed method is applied to the state estimation problem for a large power network and illustrated using the IEEE 118-bus system. It is shown that the performance of this method is close to that given by a centralized Kalman filtering approach and much better than that given by a local Kalman filtering approach, yet the computational complexity and communication load of the proposed method are low for each node, making the method scalable for large-sized networked systems.