基于图神经网络的电网流域稳定性预测
Predicting Basin Stability of Power Grids using Graph Neural Networks
摘要：
随着可再生能源在电力系统中所占比重的增加，由于其分散的结构、较小的惯性和波动性，电网的动态稳定性预测变得更加重要和具有挑战性。我们研究了应用图神经网络（GNN）预测同步在复杂电网的动态稳定性，使用单节点流域稳定性（SNBS）作为一种措施的可行性。为此，我们分别为20和100个节点的网格生成两个合成数据集，并使用Monte-Carlo抽样估计SNBS。这些数据集用于训练和评估八种不同GNN模型的性能。所有模型都使用没有简化的完整图作为输入，并在逐步回归设置中预测SNBS。我们表明，SNBS可以预测一般和性能显着变化，使用不同的GNN模型。此外，我们观察到我们方法的有趣传输能力：在较小网格上训练的GNN模型可以直接应用于较大网格，而无需重新训练。
英文摘要：
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.
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