Quickest Line Outage Detection with Low False Alarm Rate and No Prior Outage Knowledge

With a large-scale distributed energy resources (DER) penetration in distribution grids, traditional outage de-tection methods will have poor performance. This is because past methods rely on customer reports and smart meters' 'last gasp' signals, but such signatures may disappear as renewable generators, storage, and the mesh structure in urban distribution grids can continue supplying power after line outages. To capture outage signatures accurately with performance guarantees, we propose a data-driven outage monitoring approach based on the optimal change point detection method with a theoretical guarantee. However, existing change point detection methods require post-outage voltage distribution, which is unknown in distribution systems. Therefore, we design a maximum likelihood estimator for learning the distribution parameters from voltage data directly. In addition, we introduce a parameter estimation confidence interval that significantly reduces the false alarm rate that is caused by approximation errors. Theoretical proof is provided for performance guarantee. Extensive simulation results show highly accurate outage identification in various distribution grids using real smart meter data from our utility partners.