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001025663 1001_ $$0P:(DE-Juel1)184782$$aTitz, Maurizio$$b0
001025663 245__ $$aPredicting dynamic stability from static features in power grid models using machine learning
001025663 260__ $$aWoodbury, NY$$bAmerican Institute of Physics$$c2024
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001025663 520__ $$aA reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article, we propose a combination of network science metrics and machine learning models to predict the risk of desynchronization events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and, thus, reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronization events after line failures with an average precision greater than 0.996 when averaging over all datasets. Learning transfer between different datasets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronization is essentially governed by only a few network metrics that quantify the networks’ ability to reroute the flow without creating exceedingly high static line loadings.
001025663 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x0
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001025663 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x2
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001025663 7001_ $$0P:(DE-Juel1)176610$$aKaiser, Franz$$b1
001025663 7001_ $$0P:(DE-Juel1)179250$$aKruse, Johannes$$b2
001025663 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b3$$eCorresponding author
001025663 773__ $$0PERI:(DE-600)1472677-4$$a10.1063/5.0175372$$gVol. 34, no. 1, p. 013139$$n1$$p013139$$tChaos$$v34$$x1527-2443$$y2024
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