# From State Estimation to Network Reconstruction

## TL;DR

This paper introduces compressed sensing algorithms for power grid topology and parameter reconstruction, enabling efficient monitoring of grid structure and state even with limited measurements and uncertain information.

## Contribution

It presents novel compressed sensing methods that leverage network sparsity and prior knowledge to reconstruct power grid topology and parameters from minimal measurements.

## Key findings

- Algorithms effectively reconstruct grid topology and parameters.
- Methods outperform traditional state estimation in resource efficiency.
- Applicable to uncertain or incomplete grid information.

## Abstract

We develop methods to efficiently reconstruct the topology and line parameters of a power grid from the measurement of nodal variables. We propose two compressed sensing algorithms that minimize the amount of necessary measurement resources by exploiting network sparsity, symmetry of connections and potential prior knowledge about the connectivity. The algorithms are reciprocal to established state estimation methods, where nodal variables are estimated from few measurements given the network structure. Hence, they enable an advanced grid monitoring where both state and structure of a grid are subject to uncertainties or missing information.