Predicting Basin Stability of Power Grids using Graph Neural Networks

TL;DR

This paper explores the use of graph neural networks to predict the stability of power grids, demonstrating that models trained on small grids can effectively generalize to larger ones, aiding in renewable energy integration.

Contribution

It introduces a GNN-based method for predicting power grid stability and shows transferability of models across different grid sizes, which was not previously demonstrated.

Findings

GNN models can predict basin stability with reasonable accuracy.

Models trained on small grids transfer effectively to larger grids.

Performance varies significantly across different GNN architectures.

Abstract

The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure, reduced inertia and volatility. We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure. To do so, we generate two synthetic datasets for grids with 20 and 100 nodes respectively and estimate SNBS using Monte-Carlo sampling. Those datasets are used to train and evaluate the performance of eight different GNN-models. All models use the full graph without simplifications as input and predict SNBS in a nodal-regression-setup. We show that SNBS can be predicted in general and the performance significantly changes using different GNN-models. Furthermore, we observe interesting transfer capabilities of our approach: GNN-models trained on smaller grids can directly be applied on larger grids without the need of retraining.