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 …