Quick Line Outage Identification in Urban Distribution Grids via Smart Meters

TL;DR

This paper introduces a data-driven, real-time outage detection method for urban distribution grids that leverages smart meter voltage data and change-point detection theory, effectively identifying line outages even with high DER integration.

Contribution

It develops a novel outage detection approach based on stochastic time series analysis and maximum likelihood estimation, with theoretical guarantees and minimal data requirements.

Findings

Achieves high accuracy in outage detection across multiple grid configurations.

Requires only voltage magnitude data from smart meters for rapid identification.

Proven to outperform traditional methods in DER-rich urban grids.

Abstract

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.