2022/10/17 by Maurizio Titz, Titz, Maurizio, Franz Kaiser +5
Engineering · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #Data Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Optimal Power Flow Distribution #Smart Grid Security and Resilience #Statistics and Probability (physics.data-an) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2210.09266
openalex publication_date 2022/10/17 · openalex created_date 2023/02/14 · openalex updated_date 2026/07/28
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.