# Physics-Aware Neural Networks for Distribution System State Estimation

## TL;DR

This paper introduces a physics-aware neural network architecture for distribution system state estimation, leveraging grid topology and physical laws to improve accuracy and efficiency over traditional methods.

## Contribution

It presents a novel neural network design that incorporates power grid structure, reducing complexity and overfitting, along with a greedy algorithm for optimal sensor placement.

## Key findings

- Outperforms Gauss-Newton in accuracy and speed
- Reduces training complexity through structured architecture
- Effective sensor placement minimizes neural network complexity

## Abstract

The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very sensitive to the initialization and often not suitable for real-time estimation. Learning approaches are very promising for real-time estimation, as they shift the computational burden to an offline training stage. Prior machine learning approaches to power system state estimation have been electrical model-agnostic, in that they did not exploit the topology and physical laws governing the power grid to design the architecture of the learning model. In this paper, we propose a novel learning model that utilizes the structure of the power grid. The proposed neural network architecture reduces the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting the separability of the estimation problem. This prevents overfitting and reduces the complexity of the training stage. We also propose a greedy algorithm for phasor measuring units placement that aims at minimizing the complexity of the neural network required for realizing the state estimation mapping. Simulation results show superior performance of the proposed method over the Gauss-Newton approach.