Modelling power grids as pseudo adaptive networks

TL;DR

This paper explores the dynamical behavior of power grid networks by modeling them as pseudo adaptive networks using Kuramoto-Sakaguchi oscillators with inertia, revealing insights into multicluster and solitary states.

Contribution

It introduces the concept of pseudo coupling weights to analyze power grid dynamics, connecting neuronal network models with power grid behavior.

Findings

Identification of multicluster states in power grid models

Introduction of pseudo coupling weights for better analysis

Insights into solitary states in power grids

Abstract

Power grids, 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 work, we exploit the recently established relation between these two types of networks to gain insights into the dynamical properties of multifrequency clusters in power grid networks. For this, we consider the model of Kuramoto-Sakaguchi phase oscillators with inertia and describe the emergence of multicluster states. Building on this, we provide a new perspective on solitary states in power grid networks by introducing the concept of pseudo coupling weights.