CCS2021 Satellite Symposium
Data-based Diagnosis of Networked Dynamical Systems
A wide variety of natural and human-made systems consist of a large set of dynamical units coupled into a complex structure.
Breakdown of such systems can have a dramatic impact, as in the case of neurons in the brain or lines in an electric grid, to name but a few.
Preventing such catastrophic events requires in particular to be able to detect and locate the source of disturbances as fast as possible.
In this talk, I will describe a simple method to identify and locate disturbances in networks of coupled dynamical agents, relying only on time series measurements and on the knowledge of the (possibly Kron-reduced) network structure.
The strength and the appeal of the present approach lies in its simplicity paired with the ability to precisely locate disturbances and even to differentiate between line and node disturbances.
If one has access to measurement at only a subset of nodes, the method is still able to identify the location of the disturbance if the disturbed nodes are measured.
If not, one can still identify the region of the network where the disturbance occurs.
Joint work with Robin Delabays (UCSB) and Laurent Pagnier (University of Arizona).
Reference
R. Delabays, L. Pagnier, M. Tyloo New Journal of Physics 23 (4), 043037 (2021).