Inference of modes for linear stochastic processes

TL;DR

This paper introduces real-time methods to infer the modes, including damping rates, frequencies, and shapes, of linear stochastic systems from observations, with applications in electrical power networks.

Contribution

It develops novel real-time inference techniques for modes of linear stochastic systems, including complex modes, from observational data.

Findings

Effective mode inference from noisy observations

Application to power flow oscillation detection

Framework adaptable to various dynamical systems

Abstract

For dynamical systems that can be modelled as asymptotically stable linear systems forced by Gaussian noise, this paper develops methods to infer or estimate their modes from observations in real time. The modes can be real or complex. For a real mode, we wish to infer its damping rate and mode shape. For a complex mode, we wish to infer its frequency, damping rate and (complex) mode shape. Their amplitudes and correlations are encoded in a mode covariance matrix. The work is motivated and illustrated by the problem of detection of oscillations in power flow in AC electrical networks. Suggestions of other applications are given.