  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.
