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  What adaptive neuronal networks teach us about power grids

Berner, R., Yanchuk, S., Schöll, E. (2021): What adaptive neuronal networks teach us about power grids. - Physical Review E, 103, 4, 042315.
https://doi.org/10.1103/PhysRevE.103.042315

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_25568 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_25568_4
Genre: Journal Article

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 Creators:
Berner, Rico1, Author
Yanchuk, Serhiy1, Author
Schöll, Eckehard2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

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 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 past 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 discover a plethora of multicluster states for phase oscillators with inertia. Moreover, the phenomenon of cascading line failure in power grids is translated into an adaptive neuronal network.

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 Dates: 2021-04-082021-04-282021-04-28
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevE.103.042315
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Quantitative Methods
MDB-ID: No data to archive
OATYPE: Green Open Access
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Title: Physical Review E
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 103 (4) Sequence Number: 042315 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150218
Publisher: American Physical Society (APS)