  Recently, distributed algorithms for power system state estimation have
attracted significant attention. Along with such advantages as decomposition,
parallelization of the original problem and absence of a central computation
unit, distributed state estimation may also serve for local information privacy
reasons since the only information to be transferred is the boundary states of
neighboring areas. In this paper, we propose some novel approaches for speeding
up the ADMM-based distributed state estimation algorithms by utilizing some
recent results in optimization theory. We also thoroughly analyze the
theoretical and practical performance, concluding that accelerated approach
outperforms the existing ones. The theoretical considerations are verified
through the experiments on a scalable example.
