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• W3034195639 abstract "Power grid networks, as well as neuronal networks with synaptic plasticity, describe real-world systems of tremendous importance for our daily life. The investigation of these seemingly unrelated types of dynamical networks has attracted increasing attention over the last decade. In this paper, we provide insight into the fundamental relation between these two types of networks. For this, we consider well-established models based on phase oscillators and show their intimate relation. In particular, we prove that phase oscillator models with inertia can be viewed as a particular class of adaptive networks. This relation holds even for more general classes of power grid models that include voltage dynamics. As an immediate consequence of this relation, we find a novel type of multicluster state for phase oscillators with inertia. Moreover, the phenomenon of cascading line failure in power grids is translated into an adaptive neuronal network." @default.
• W3034195639 created "2020-06-19" @default.
• W3034195639 creator A5020803074 @default.
• W3034195639 creator A5021516635 @default.
• W3034195639 creator A5062479318 @default.
• W3034195639 date "2021-04-28" @default.
• W3034195639 modified "2023-10-15" @default.
• W3034195639 title "What adaptive neuronal networks teach us about power grids" @default.
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• W3034195639 doi "https://doi.org/10.1103/physreve.103.042315" @default.
• W3034195639 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34005899" @default.
• W3034195639 hasPublicationYear "2021" @default.
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