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<p>Critical transitions necessitate anticipation to prevent adverse outcomes. While many studies focus on bifurcation-induced tipping, noise-induced tipping is also possible. We propose to use the open-source (non-Markovian) Bayesian Langevin estimation to quantify deterministic and stochastic dynamics simultaneously. By analysing bus voltage frequency time series from the Western Interconnection blackout on 10th August 1996, complemented by conceptual network models of its key events, we reveal the interplay of changing local restoring rates and noise levels. Furthermore, a comparison of these findings to the blackout’s timeline supports our frequency Langevin model driven by correlated noise. A state change is indicated two minutes before the official triggering event, potentially by establishing a tree-to-line fault. This study highlights the importance of distinguishing destabilising factors for anticipating critical transitions and provides a tool for understanding such events across various disciplines.</p>

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Quantifying local stability and noise levels from time series in the US Western Interconnection blackout on 10th August 1996

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  • 发表日期：

    2025年7月7日

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  • Martin Heßler,
  • Oliver Kamps

摘要

Critical transitions necessitate anticipation to prevent adverse outcomes. While many studies focus on bifurcation-induced tipping, noise-induced tipping is also possible. We propose to use the open-source (non-Markovian) Bayesian Langevin estimation to quantify deterministic and stochastic dynamics simultaneously. By analysing bus voltage frequency time series from the Western Interconnection blackout on 10th August 1996, complemented by conceptual network models of its key events, we reveal the interplay of changing local restoring rates and noise levels. Furthermore, a comparison of these findings to the blackout’s timeline supports our frequency Langevin model driven by correlated noise. A state change is indicated two minutes before the official triggering event, potentially by establishing a tree-to-line fault. This study highlights the importance of distinguishing destabilising factors for anticipating critical transitions and provides a tool for understanding such events across various disciplines.