Predicting basin stability of power grids using graph neural networks

New Journal of Physics, vol. 24, pp. 043041

Abstract

Abstract 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.

Authors 8

1. Christian Nauck

   Potsdam Institute for Climate Impact Research

   Affiliation as printed

   Department of Complexity Science, Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, Potsdam, Brandenburg 14473, Germany

2. Michael Lindner

   Potsdam Institute for Climate Impact Research

   Affiliation as printed

   Department of Complexity Science, Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, Potsdam, Brandenburg 14473, Germany

3. Konstantin Schürholt

   University of St.Gallen

   Affiliation as printed

   Institute of Computer Science, University of St. Gallen, Rosenbergstrasse 30, St. Gallen 9000, Switzerland

4. Haoming Zhang Aachen Cybernetics Lab IMA & IfU

   RWTH Aachen University

   Affiliation as printed

   Cybernetics Lab IMA & IfU, RWTH Aachen University, Dennewartstraße 27, Aachen 52068, Germany

5. Paul Schultz

   50Hertz Transmission GmbH (Germany)

   Affiliation as printed

   50Hertz Transmission GmbH, Elia Group, Heidestraße 2, Berlin 10557, Germany

6. Jürgen Kurths

   Potsdam Institute for Climate Impact Research

   Affiliation as printed

   Department of Complexity Science, Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, Potsdam, Brandenburg 14473, Germany

7. Ingrid Isenhardt Aachen Cybernetics Lab IMA & IfU

   RWTH Aachen University

   Affiliation as printed

   Cybernetics Lab IMA & IfU, RWTH Aachen University, Dennewartstraße 27, Aachen 52068, Germany

8. Frank Hellmann corresponding

   Potsdam Institute for Climate Impact Research

   Affiliation as printed

   Department of Complexity Science, Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, Potsdam, Brandenburg 14473, Germany