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

arXiv: https://arxiv.org/abs/1912.04992 · HTML: https://mlsanity.com/paper/1912.04992/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network](https://mlsanity.com/paper/2108.00599/) (2021)
- [SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models](https://mlsanity.com/paper/2601.21706/) (2026)

## Abstract (arXiv)

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.