The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With 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 renewable generators and storage 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 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 are unknown in distribution systems. Therefore

we design a maximum likelihood estimator to directly learn distribution pa-rameters from voltage data. We prove the estimated parameters-based detection also achieves 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 17 configurations with and without DERs using smart meter data.