  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.
