# Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee

arXiv: https://arxiv.org/abs/2309.07157 · HTML: https://mlsanity.com/paper/2309.07157/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Non-convex Optimization for Machine Learning](https://mlsanity.com/paper/1712.07897/) (2017)
- [Urban MV and LV Distribution Grid Topology Estimation via Group Lasso](https://mlsanity.com/paper/1611.01845/) (2016)
- [Robust Sequential Change-Point Detection by Convex Optimization](https://mlsanity.com/paper/1701.06952/) (2017)
- [Structural Damage Detection and Localization with Unknown Post-Damage Feature Distribution Using Sequential Change-Point Detection Method](https://mlsanity.com/paper/1812.02824/) (2018)
- [On the Properties of the Power Systems Nodal Admittance Matrix](https://mlsanity.com/paper/1702.07235/) (2017)
- [Quick Line Outage Identification in Urban Distribution Grids via Smart Meters](https://mlsanity.com/paper/2104.02056/) (2021)

## Built on by (most cited first, 5 of 5)

- [Solar Photovoltaic Assessment with Large Language Model](https://mlsanity.com/paper/2507.19144/) (2025)
- [PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis](https://mlsanity.com/paper/2509.12212/) (2025)
- [Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning](https://mlsanity.com/paper/2510.03578/) (2025)
- [Owen-based Semantics and Hierarchy-Aware Explanation (O-Shap)](https://mlsanity.com/paper/2602.17107/) (2026)
- [Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution](https://mlsanity.com/paper/2608.03878/) (2026)

## Abstract (arXiv)

Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.