State Estimation in Electric Power Systems Leveraging Graph Neural Networks

TL;DR

This paper introduces a graph neural network approach for fast and accurate state estimation in electric power systems using PMU data, aiming to improve real-time monitoring capabilities.

Contribution

It presents a novel GNN-based method trained on synthetic data to enhance speed and accuracy of power system state estimation with PMUs.

Findings

GNN achieves high accuracy in various test scenarios.

The method is sensitive to missing input data but remains effective.

Training on synthetic data enables robust predictions.

Abstract

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.