  The growing integration of distributed energy resources (DERs) in
distribution grids raises various reliability issues due to DER's uncertain and
complex behaviors. With a large-scale DER penetration in distribution grids,
traditional outage detection methods, which rely on customers report and smart
meters' last gasp signals, will have poor performance, because the renewable
generators and storages and the mesh structure in urban distribution grids can
continue supplying power after line outages. To address these challenges, we
propose a data-driven outage monitoring approach based on the stochastic time
series analysis with a theoretical guarantee. Specifically, we prove via power
flow analysis that the dependency of time-series voltage measurements exhibits
significant statistical changes after line outages. This makes the theory on
optimal change-point detection suitable to identify line outages. However,
existing change point detection methods require post-outage voltage
distribution, which is unknown in distribution systems. Therefore, we design a
maximum likelihood estimator to directly learn the distribution parameters from
voltage data. We prove that the estimated parameters-based detection also
achieves the optimal performance, making it extremely useful for fast
distribution grid outage identifications. Furthermore, since smart meters have
been widely installed in distribution grids and advanced infrastructure (e.g.,
PMU) has not widely been available, our approach only requires voltage
magnitude for quick outage identification. Simulation results show highly
accurate outage identification in eight distribution grids with 14
configurations with and without DERs using smart meter data.
