Continuous-Time Echo State Networks for Predicting Power System Dynamics

Ciaran Roberts, Jose Lara, Rodrigo Henriquez-Auba, Matthew Bossart, Ranjan Anantharaman, Chris Rackauckas, Bri-Mathias Hodge, Duncan Callaway

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations

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.

Original languageAmerican English
Article number108562
Number of pages7
JournalElectric Power Systems Research
Volume212
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors

NREL Publication Number

  • NREL/JA-6A40-83775

Keywords

  • Data-driven modeling techniques
  • Electro-magnetic transients
  • Machine learning
  • Power system dynamics

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