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
dynamic stability of power grids. We provide new datasets of dynamic stability
of synthetic power grids and find that GNNs are surprisingly effective at
predicting the highly non-linear targets from topological information only.
Furthermore, we use GNNs to demonstrate the accurate identification of
particularly vulnerable nodes in power grids, so-called troublemakers. Lastly,
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 of the presented approach.

为了应对气候变化，需要提高可再生能源在电力生产中的份额。在分散化、惯性减小和生产波动性等方面，可再生能源为电力网络引入了新的挑战。本研究利用图神经网络以分析电力网动态稳定性的计算工作量，发现其在仅基于拓扑信息的情况下预测非线性目标非常有效。此外，应用所提出的方法，还能准确识别出电力网的易受攻击的节点。最后，发现在小型电力网上训练的 GNN 在对得克萨斯电力网的大型模型进行预测时，也具有准确性和实用性。