Predicting dynamic stability from static features in power grid models using machine learning

Maurizio Titz, Franz J. Kaiser, Johannes Kruse, Dirk Witthaut

Chaos An Interdisciplinary Journal of Nonlinear Science 2024 article cited by 15

importance 7/10 partially solved Power System Optimization and Stability power grids stage: research

Predicting the risk of power grid desynchronization after transmission line failures, a major threat to grid stability.

Transmission line failures can split the grid into asynchronous fragments, threatening reliable power supply.

Machine learning models combined with network science metrics predicted desynchronization events after line failures and revealed key robustness factors via feature selection.

Average precision greater than 0.996 on simulated data from synthetic test grids

The evidence supports high-precision prediction, but only on simulated synthetic grids, not real power systems.

Venue Chaos An Interdisciplinary Journal of Nonlinear Science
Published 2024
Work type article
Authors Maurizio Titz, Franz J. Kaiser, Johannes Kruse, Dirk Witthaut
Citations 15 (OpenAlex, at import time)
References 51
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.1063/5.0175372
Open access closed
Keywords power grid stability, machine learning, desynchronization, network science, transmission lines
7 / 10

significant — thousands to millions affected

Abstract states reliable electric power supply is vital for society and line failures are among the biggest threats to grid stability; validated only on synthetic grids.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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