@article{35499, author = {Ciaran Roberts and José Daniel Lara and Rodrigo Henriquez-Auba and Matthew Bossart and Ranjan Anantharaman and Chris Rackauckas and Bri-Mathias Hodge and Duncan S Callaway}, title = {Continuous-time echo state networks for predicting power system dynamics}, 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 $\pm 4$ 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.

}, year = {2022}, journal = {Electric Power Systems Research}, volume = {212}, pages = {108562}, month = {11/2022}, issn = {03787796}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0378779622006587}, doi = {10.1016/j.epsr.2022.108562}, language = {eng}, }