Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems

TL;DR

This paper introduces Power-GNN, a physics-informed Graph Neural Network that improves parameter and state estimation in power systems by embedding physical models, leading to more reliable and interpretable real-time predictions.

Contribution

The paper presents a novel hybrid GNN approach that incorporates power system physics into deep learning for improved parameter and state estimation.

Findings

Power-GNN outperforms standard neural networks in power system estimation tasks.

It reconstructs physically interpretable parameters like admittances.

The method is effective on large, realistic power network datasets.

Abstract

Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread tasks in the system engineering. They need to be done automatically, fast and frequently, as measurements arrive. Deep Learning (DL) holds the promise of tackling the challenge, however in so far, as PE and SE in power systems is concerned, (a) DL did not win trust of the system operators because of the lack of the physics of electricity based, interpretations and (b) DL remained illusive in the operational regimes were data is scarce. To address this, we present a hybrid scheme which embeds physics modeling of power systems into Graphical Neural Networks (GNN), therefore empowering system operators with a reliable and explainable real-time predictions which can then be used to control the critical infrastructure. To enable progress towards trustworthy DL for PE and SE, we build a physics-informed method, named Power-GNN, which reconstructs physical, thus interpretable, parameters within Effective Power Flow (EPF) models, such as admittances of effective power lines, and NN parameters, representing implicitly unobserved elements of the system. In our experiments, we test the Power-GNN on different realistic power networks, including these with thousands of loads and hundreds of generators. We show that the Power-GNN outperforms vanilla NN scheme unaware of the EPF physics.