Urban distribution grid line outage identification

The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to complex uncertainties. With the large-scale penetration of DERs, traditional outage detection methods, which rely on customers making phone calls and smart meters' “last gasp” signals, will have limited performance because 1) the renewable generators can supply powers after line outages, and 2) many urban grids are mesh and line outages do not affect power supply. To address these drawbacks, we propose a new data-driven outage monitoring approach based on the stochastic time series analysis with the newly available smart meter data utilized. Specifically, based on the power flow analysis, we prove that the statistical dependency of time-series voltage measurements has significant changes after line outages. Hence, we use the optimal change point detection theory to detect and localize line outages. As the existing change point detection methods require the post-outage voltage distribution, which is unknown in power systems, we propose a maximum likelihood method to learn the distribution parameters from the historical data. The proposed outage detection using estimated parameters also achieves the optimal performance. Simulation results show highly accurate outage identification in IEEE standard distribution test systems with and without DERs using real smart meter data.