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  Predicting basin stability of power grids using graph neural networks

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

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_26864 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_26864_6
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https://creativecommons.org/licenses/by/4.0/

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https://zenodo.org/record/5148085 (???ENUM_CONTENTCATEGORY_supplementary-material???)
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 ???ViewItemFull_lblCreators???:
Nauck, Christian1, ???ENUM_CREATORROLE_AUTHOR???           
Lindner, Michael1, ???ENUM_CREATORROLE_AUTHOR???           
Schürholt, Konstantin2, ???ENUM_CREATORROLE_AUTHOR???
Zhang, Haoming2, ???ENUM_CREATORROLE_AUTHOR???
Schultz, Paul1, ???ENUM_CREATORROLE_AUTHOR???           
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
Isenhardt, Ingrid2, ???ENUM_CREATORROLE_AUTHOR???
Hellmann, Frank1, ???ENUM_CREATORROLE_AUTHOR???                 
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1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              
2External Organizations, ou_persistent22              

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???ViewItemFull_lblSubject???: Complex Systems, Nonlinear Dynamics, Dynamic Stability, Basin Stability, Power Grids, Machine Learning, Graph Neural Networks
 ???ViewItemFull_lblAbstract???: 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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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2021-12-162022-02-162022-02-162022-04
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1088/1367-2630/ac54c9
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
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???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Energy
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_MDB_ID???: No MDB - stored outside PIK (see DOI)
???ENUM_IDENTIFIERTYPE_OATYPE???: Gold Open Access
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???project_info_title??? : Gefördert im Rahmen des Förderprogramms "Open Access Publikationskosten" durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491075472.
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???project_info_funding_info_program_title??? : Open-Access-Publikationskosten (491075472)
???project_info_funding_info_organization_title??? : Deutsche Forschungsgemeinschaft (DFG)

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???ViewItemFull_lblSourceTitle???: New Journal of Physics
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3, oa
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???ENUM_IDENTIFIERTYPE_PUBLISHER???: IOP Publishing