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Predicting basin stability of power grids using graph neural networks

Urheber*innen
/persons/resource/nauck

Nauck,  Christian
Potsdam Institute for Climate Impact Research;

/persons/resource/Michael.Lindner

Lindner,  Michael
Potsdam Institute for Climate Impact Research;

Schürholt,  Konstantin
External Organizations;

Zhang,  Haoming
External Organizations;

/persons/resource/Paul.Schultz

Schultz,  Paul
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Isenhardt,  Ingrid
External Organizations;

/persons/resource/frank.hellmann

Hellmann,  Frank
Potsdam Institute for Climate Impact Research;

Externe Ressourcen

https://zenodo.org/record/5148085
(Ergänzendes Material)

Volltexte (frei zugänglich)

26864oa.pdf
(Verlagsversion), 4MB

Ergänzendes Material (frei zugänglich)
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Zitation

Nauck, C., Lindner, M., Schürholt, K., Zhang, H., Schultz, P., Kurths, J., Isenhardt, I., Hellmann, F. (2022): Predicting basin stability of power grids using graph neural networks. - New Journal of Physics, 24, 043041.
https://doi.org/10.1088/1367-2630/ac54c9


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_26864
Zusammenfassung
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.