Outage Detection in Partially Observable Distribution Systems Using Smart Meters and Generative Adversarial Networks

In this paper, we present a novel data-driven approach to detect outage events in partially observable distribution systems by capturing the changes in smart meters' (SMs) data distribution. To achieve this, first, a breadth-first search (BFS)-based mechanism is proposed to decompose the network into a set of zones that maximize outage location information in partially observable systems. Then, using SM data in each zone, a generative adversarial network (GAN) is designed to implicitly extract the temporal-spatial behavior in normal conditions in an unsupervised fashion. After training, an anomaly scoring technique is leveraged to determine if real-time measurements indicate an outage event in the zone. Finally, to infer the location of the outage events in a multi-zone network, a zone coordination process is proposed to take into account the interdependencies of intersecting zones. We have provided analytical guarantees of performance for our algorithm using the concept of entropy , which is leveraged to quantify outage location information in multi-zone grids. The proposed method has been tested and verified on distribution feeder models with real SM data.