Predicting Basin Stability of Power Grids using Graph Neural Networks
This addresses stability prediction for power grids with renewable energy, but it is incremental as it applies existing GNN methods to a new domain-specific dataset.
The study tackled predicting dynamic stability in power grids using graph neural networks (GNNs) to estimate single-node basin stability (SNBS), showing that SNBS can be predicted and models trained on smaller grids (20 nodes) transfer to larger ones (100 nodes) without retraining.
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.