Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate Article Swipe
Matthew Bossart
,
José Daniel Lara
,
Ciaran Roberts
,
Rodrigo Henriquez-Auba
,
Duncan S. Callaway
,
Bri‐Mathias Hodge
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.06827
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.06827
The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. In this paper, we propose a data-driven surrogate model based on implicit machine learning -- specifically deep equilibrium layers and neural ordinary differential equations -- to learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.06827
- https://arxiv.org/pdf/2405.06827
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396913083
All OpenAlex metadata
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https://openalex.org/W4396913083Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.06827Digital Object Identifier
- Title
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Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE SurrogateWork title
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-10Full publication date if available
- Authors
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Matthew Bossart, José Daniel Lara, Ciaran Roberts, Rodrigo Henriquez-Auba, Duncan S. Callaway, Bri‐Mathias HodgeList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.06827Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2405.06827Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2405.06827Direct OA link when available
- Concepts
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Ode, Acceleration, Artificial neural network, Power (physics), Computer science, Control theory (sociology), Layer (electronics), Mechanics, Applied mathematics, Mathematics, Physics, Classical mechanics, Artificial intelligence, Materials science, Thermodynamics, Control (management), Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |