Journal Article

FZJ-2024-03052

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Predicting dynamic stability from static features in power grid models using machine learning

Titz, M.FZJ* ; Kaiser, F. ; Kruse, J.FZJ* ; Witthaut, D. (Corresponding author)FZJ*

2024
American Institute of Physics Woodbury, NY

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Please use a persistent id in citations: doi:10.1063/5.0175372  doi:10.34734/FZJ-2024-03052

Abstract: 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 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.

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Contributing Institute(s):

1. Modellierung von Energiesystemen (IEK-10)

Research Program(s):

1. 1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112) (POF4-112)
2. Verbundvorhaben CoNDyNet2: Kollektive nichtlineare Dynamik komplexer Stromnetze (03EK3055B) (03EK3055B)
3. HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) (HDS-LEE-20190612)

Appears in the scientific report 2024

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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