Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate Article Swipe
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· 2024
· Open Access
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· 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.
Related Topics To Compare & Contrast
- 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