# Graphical Models in Meshed Distribution Grids: Topology estimation, change detection and limitations

arXiv: https://arxiv.org/abs/1905.06550 · HTML: https://mlsanity.com/paper/1905.06550/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

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

- [Learning Distribution Grid Topologies: A Tutorial](https://mlsanity.com/paper/2206.10837/) (2022)
- [Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services](https://mlsanity.com/paper/2307.01684/) (2023)
- [Learning the Structure of Large Networked Systems Obeying Conservation Laws](https://mlsanity.com/paper/2206.07083/) (2022)
- [Robust Model Selection of Gaussian Graphical Models](https://mlsanity.com/paper/2211.05690/) (2022)
- [Learning Networks from Wide-Sense Stationary Stochastic Processes](https://mlsanity.com/paper/2412.03768/) (2024)

## Abstract (arXiv)

Graphical models are a succinct way to represent the structure in probability distributions. This article analyzes the graphical model of nodal voltages in non-radial power distribution grids. Using algebraic and structural properties of graphical models, algorithms exactly determining topology and detecting line changes for distribution grids are presented along with their theoretical limitations. We show that if distribution grids have cycles/loops of size greater than three, then nodal voltages are sufficient for efficient topology estimation without additional assumptions on system parameters. In contrast, line failure or change detection using nodal voltages does not require any structural assumption. Under noisy measurements, we provide the first non-trivial bounds on the maximum noise that the system can tolerate for asymptotically correct topology recovery. The performance of the designed algorithms is validated with nonlinear AC power flow samples generated by Matpower on test grids, including scenarios with injection correlations and system noise.