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Add to EdgePredicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
Feb 27, 2024
A central question of network science is how functional properties of systems arise from their structure. For networked dynamical systems, structure is typically quantified with network measures. A functional property that is of theoretical and practical interest for oscillatory systems is the stability of synchrony to localized perturbations. Recently, Graph Neural Networks (GNNs) have been shown to predict this stability successfully; at the same time, network measures have struggled to paint a clear picture. Here we collect 46 relevant network measures and find that no small subset can reliably predict stability. The performance of GNNs can only be matched by combining all network measures and nodewise machine learning. However, unlike GNNs, this approach fails to extrapolate from network ensembles to several real power grid topologies. This suggests that correlations of network measures and function may be misleading, and that GNNs capture the causal relationship between structure and stability substantially better.
Towards dynamic stability analysis of sustainable power grids using graph neural networks
Dec 21, 2022
To mitigate climate change, the share of renewable needs to be increased. Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and volatility in production. The operation of sustainable power grids with a high penetration of renewable energies requires new methods to analyze the dynamic stability. We provide new datasets of dynamic stability of synthetic power grids and find that graph neural networks (GNNs) are surprisingly effective at predicting the highly non-linear target from topological information only. To illustrate the potential to scale to real-sized power grids, we demonstrate the successful prediction on a Texan power grid model.
* main section: 4 pages, 3 figures
Dynamic stability of power grids -- new datasets for Graph Neural Networks
Jun 10, 2022
One of the key challenges for the success of the energy transition towards renewable energies is the analysis of the dynamic stability of power grids. However, dynamic solutions are intractable and exceedingly expensive for large grids. Graph Neural Networks (GNNs) are a promising method to reduce the computational effort of predicting dynamic stability of power grids, however datasets of appropriate complexity and size do not yet exist. We introduce two new datasets of synthetically generated power grids. For each grid, the dynamic stability has been estimated using Monte-Carlo simulations. The datasets have 10 times more grids than previously published. To evaluate the potential for real-world applications, we demonstrate the successful prediction on a Texan power grid model. The performance can be improved to surprisingly high levels by training more complex models on more data. Furthermore, the investigated grids have different sizes, enabling the application of out-of-distribution evaluation and transfer learning from a small to a large domain. We invite the community to improve our benchmark models and thus aid the energy transition with better tools.
Predicting Dynamic Stability of Power Grids using Graph Neural Networks
Aug 18, 2021
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.