Continuous-time echo state networks for predicting power system dynamics

Publication Type

Journal Article

Date Published

11/2022

Authors

DOI

Abstract

With the growing penetration of converter-interfaced generation in power systems, the dynamical behavior of these systems is rapidly evolving. One of the challenges with converter-interfaced generation is the increased number of equations, as well as the required numerical timestep, involved in simulating these systems. Within this work, we explore the use of continuous-time echo state networks as a means to cheaply, and accurately, predict the dynamic response of power systems subject to a disturbance for varying system parameters. We show an application for predicting frequency dynamics following a loss of generation for varying penetrations of grid-following and grid-forming converters. We demonstrate that, after training on 20 solutions of the full-order system, we achieve a median nadir prediction error of 0.17 mHz with 95% of all nadir prediction errors within ±4 mHz. We conclude with some discussion on how this approach can be used for parameter sensitivity analysis and within optimization algorithms to rapidly predict the dynamical behavior of the system.

Journal

Electric Power Systems Research

Volume

212

Year of Publication

2022

URL

ISSN

03787796

Organization