Open AccessLow-frequency oscillations in coupled phase oscillators with inertia
Author(s) -
Huihui Song,
Xuewei Zhang,
J. F. Wu,
Yi Qu
Publication year - 2019
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/s41598-019-53953-1
Subject(s) - kuramoto model , coupling (piping) , forcing (mathematics) , node (physics) , work (physics) , inertia , phase (matter) , physics , stability (learning theory) , control theory (sociology) , power (physics) , resonance (particle physics) , statistical physics , computer science , synchronization (alternating current) , classical mechanics , acoustics , telecommunications , materials science , channel (broadcasting) , control (management) , atmospheric sciences , quantum mechanics , machine learning , artificial intelligence , particle physics , metallurgy , thermodynamics
This work considers a second-order Kuramoto oscillator network periodically driven at one node to model low-frequency forced oscillations in power grids. The phase fluctuation magnitude at each node and the disturbance propagation in the network are numerically analyzed. The coupling strengths in this work are sufficiently large to ensure the stability of equilibria in the unforced system. It is found that the phase fluctuation is primarily determined by the network structural properties and forcing parameters, not the parameters specific to individual nodes such as power and damping. A new “resonance” phenomenon is observed in which the phase fluctuation magnitudes peak at certain critical coupling strength in the forced system. In the cases of long chain and ring-shaped networks, the Kuramoto model yields an important but somehow counter-intuitive result that the fluctuation magnitude distribution does not necessarily follow a simple attenuating trend along the propagation path and the fluctuation at nodes far from the disturbance source could be stronger than that at the source. These findings are relevant to low-frequency forced oscillations in power grids and will help advance the understanding of their dynamics and mechanisms and improve the detection and mitigation techniques.