# Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learning

arXiv: https://arxiv.org/abs/2210.09266 · HTML: https://mlsanity.com/paper/2210.09266/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Shapley values for feature selection: The good, the bad, and the axioms](https://mlsanity.com/paper/2102.10936/) (2021)
- [Small vulnerable sets determine large network cascades in power grids](https://mlsanity.com/paper/1804.06432/) (2018)
- [PowerDynamics.jl -- An experimentally validated open-source package for the dynamical analysis of power grids](https://mlsanity.com/paper/2101.02103/) (2021)
- [Transmission needs across a fully renewable European power system](https://mlsanity.com/paper/1306.1079/) (2013)
- [Dual theory of transmission line outages](https://mlsanity.com/paper/1606.07276/) (2016)
- [Emergent failures and cascades in power grids: a statistical physics perspective](https://mlsanity.com/paper/1709.10166/) (2017)

## Built on by (most cited first, 4 of 4)

- [Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning](https://mlsanity.com/paper/2406.08917/) (2024)
- [SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2312.02646/) (2023)
- [Nonlinear dynamical social and political prediction algorithm for city planning and public participation using the Impulse Pattern Formulation](https://mlsanity.com/paper/2404.00977/) (2024)
- [Graph Machine Learning: An Opportunity for Power Systems](https://mlsanity.com/paper/2608.16494/) (2026)

## Abstract (arXiv)

A 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 desynchronisation 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 desynchronisation events after line failures with an average precision greater than 0.996 when averaging over all data sets. Learning transfer between different data sets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronisation is essentially governed by only a few network metrics that quantify the networks ability to reroute flow without creating exceedingly high static line loadings.