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

## TL;DR

This paper explores how graphical models can be used to accurately estimate the topology and detect changes in meshed power distribution grids, even under noisy conditions, with proven theoretical limits and validated algorithms.

## Contribution

It introduces algorithms for topology estimation and change detection in meshed distribution grids based on graphical models, along with their theoretical limitations and noise bounds.

## Key findings

- Algorithms accurately estimate grid topology from nodal voltages.
- Line failure detection is possible without structural assumptions.
- Validated performance with nonlinear power flow simulations.

## Abstract

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.