  With the rising penetration of distributed energy resources, distribution
system control and enabling techniques such as state estimation have become
essential to distribution system operation. However, traditional state
estimation techniques have difficulty coping with the low-observability
conditions often present on the distribution system due to the paucity of
sensors and heterogeneity of measurements. To address these limitations, we
propose a distribution system state estimation algorithm that employs matrix
completion (a tool for estimating missing values in low-rank matrices)
augmented with noise-resilient power flow constraints. This method operates
under low-observability conditions where standard least-squares-based methods
cannot operate, and flexibly incorporates any network quantities measured in
the field. We empirically evaluate our method on the IEEE 33- and 123-bus test
systems, and find that it provides near-perfect state estimation performance
(within 1\% mean absolute percent error) across many low-observability data
availability regimes.
