  To mitigate climate change, the share of renewable energies in power
production needs to be increased. Renewables introduce new challenges to power
grids regarding the dynamic stability due to decentralization, reduced inertia,
and volatility in production. Since dynamic stability simulations are
intractable and exceedingly expensive for large grids, graph neural networks
(GNNs) are a promising method to reduce the computational effort of analyzing
the dynamic stability of power grids. As a testbed for GNN models, we generate
new, large datasets of dynamic stability of synthetic power grids, and provide
them as an open-source resource to the research community. We find that GNNs
are surprisingly effective at predicting the highly non-linear targets from
topological information only. For the first time, performance that is suitable
for practical use cases is achieved. Furthermore, we demonstrate the ability of
these models to accurately identify particular vulnerable nodes in power grids,
so-called troublemakers. Last, we find that GNNs trained on small grids
generate accurate predictions on a large synthetic model of the Texan power
grid, which illustrates the potential for real-world applications.
