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@ARTICLE{Titz:1025663,
      author       = {Titz, Maurizio and Kaiser, Franz and Kruse, Johannes and
                      Witthaut, Dirk},
      title        = {{P}redicting dynamic stability from static features in
                      power grid models using machine learning},
      journal      = {Chaos},
      volume       = {34},
      number       = {1},
      issn         = {1527-2443},
      address      = {Woodbury, NY},
      publisher    = {American Institute of Physics},
      reportid     = {FZJ-2024-03052},
      pages        = {013139},
      year         = {2024},
      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.},
      cin          = {IEK-10},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1122 - Design, Operation and Digitalization of the Future
                      Energy Grids (POF4-112) / Verbundvorhaben CoNDyNet2:
                      Kollektive nichtlineare Dynamik komplexer Stromnetze
                      (03EK3055B) / HDS LEE - Helmholtz School for Data Science in
                      Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-1122 / G:(BMBF)03EK3055B /
                      G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38271632},
      UT           = {WOS:001150407400003},
      doi          = {10.1063/5.0175372},
      url          = {https://juser.fz-juelich.de/record/1025663},
}