Deciphering the imprint of topology on nonlinear dynamical network stability

TL;DR

This paper investigates how the topology of coupled oscillator networks influences their nonlinear stability, revealing new asymptotic states and providing a classification scheme for nodes based on their topological features.

Contribution

It extends previous work by integrating survivability and basin stability measures, uncovering novel states and establishing a topological classification for stability analysis.

Findings

Identification of solitary, desynchronized oscillators after perturbations.

Topological classification scheme for nodes in tree-shaped appendices.

Homogeneous network topologies enhance nonlinear stability.

Abstract

Coupled oscillator networks show a complex interrelations between topological characteristics of the network and the nonlinear stability of single nodes with respect to large but realistic perturbations. We extend previous results on these relations by incorporating sampling-based measures of the transient behaviour of the system, its survivability, as well as its asymptotic behaviour, its basin stability. By combining basin stability and survivability we uncover novel, previously unknown asymptotic states with solitary, desynchronized oscillators which are rotating with a frequency different from their natural one. They occur almost exclusively after perturbations at nodes with specific topological properties. More generally we confirm and significantly refine the results on the distinguished role tree-shaped appendices play for nonlinear stability. We find a topological classification scheme for nodes located in such appendices, that exactly separates them according to their stability properties, thus establishing a strong link between topology and dynamics. Hence, the results can be used for the identification of vulnerable nodes in power grids or other coupled oscillator networks. From this classification we can derive general design principles for resilient power grids. We find that striving for homogeneous network topologies facilitates a better performance in terms of nonlinear dynamical network stability. While the employed second-order Kuramoto-like model is parametrized to be representative for power grids, we expect these insights to transfer to other critical infrastructure systems or complex network dynamics appearing in various other fields.