Continuous-time echo state networks for predicting power system dynamics Article Swipe
Ciaran Roberts
,
José Daniel Lara
,
Rodrigo Henriquez-Auba
,
Matthew Bossart
,
Ranjan Anantharaman
,
Christopher Rackauckas
,
Bri‐Mathias Hodge
,
Duncan S. Callaway
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.epsr.2022.108562
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.epsr.2022.108562
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.
Related Topics
Concepts
Electric power system
Computer science
Control theory (sociology)
System dynamics
Converters
Echo (communications protocol)
Dynamical systems theory
Sensitivity (control systems)
Power (physics)
Grid
Electronic engineering
Engineering
Mathematics
Artificial intelligence
Physics
Control (management)
Computer network
Geometry
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.epsr.2022.108562
- OA Status
- hybrid
- Cited By
- 14
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285585250
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285585250Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.epsr.2022.108562Digital Object Identifier
- Title
-
Continuous-time echo state networks for predicting power system dynamicsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-07-15Full publication date if available
- Authors
-
Ciaran Roberts, José Daniel Lara, Rodrigo Henriquez-Auba, Matthew Bossart, Ranjan Anantharaman, Christopher Rackauckas, Bri‐Mathias Hodge, Duncan S. CallawayList of authors in order
- Landing page
-
https://doi.org/10.1016/j.epsr.2022.108562Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.epsr.2022.108562Direct OA link when available
- Concepts
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Electric power system, Computer science, Control theory (sociology), System dynamics, Converters, Echo (communications protocol), Dynamical systems theory, Sensitivity (control systems), Power (physics), Grid, Electronic engineering, Engineering, Mathematics, Artificial intelligence, Physics, Control (management), Computer network, Geometry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 4, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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