Distributed Power System State Estimation Using Factor Graphs

We propose a distributed and a dynamic algorithm for a power system state estimation. We model the dependencies among the state vectors of neighboring areas and among the state vectors at different times using a factor graph. We then derive message update rules and use these rules to implement a sum-product message passing algorithm on the graph. In message passing, neighboring areas exchange messages which represent their beliefs about the unknown state vectors based on all the related measurements. These beliefs are then used to compute the posterior distribution of the power system state. In our paper, we represent the messages using a particle based approximation. Such a particle-based representation provides a simple and a computationally feasible method to update the messages in each iteration. Further, it allows us to model the nonlinearities present in the power system, and hence leads to a better performance accuracy compared with the traditional methods that use linear models. We show the accuracy of the proposed method via numerical simulations using the IEEE 14 and 118 bus systems as examples.