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.

翻译：由于可再生能源的分散结构、惯性减少和波动性,对电网动态稳定性的预测变得更加重要和具有挑战性,因为可再生能源份额的增加是其分散结构、惯性减少和挥发性。我们调查了应用图形神经网络(GNNN)来预测复杂电网同步化动态稳定性的可行性,作为一种衡量标准,使用单节盆地稳定性(SNBS)来预测复杂电网同步化的动态稳定性。为此,我们分别为20和100个节点的电网制作了两个合成数据集,并使用蒙特-卡洛取样对SNBS进行了估计。这些数据集被用来训练和评价8个不同的GNNN模型的性能。所有模型都使用完整的图形作为输入,并在节点回归设置中预测SNBS。我们表明,SNBS可以总体预测,使用不同的GNNM模型可以显著变化。此外,我们看到我们的方法的转移能力很有趣:在较小的电网上培训的GNNN模可以直接应用在更大的电网上,无需再培训。