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

Quick Line Outage Identification in Urban Distribution Grids via Smart Meters