Neuro-Reachability of Networked Microgrids
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Abstract (arXiv)
A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties. Three new contributions are presented: 1) An ODENet-enabled dynamic model discovery approach is devised to construct the data-driven state-space model which preserves the nonlinear and differential structure of the NMs system; 2) A physics-data-integrated (PDI) NMs model is established, which empowers various NM analytics; and 3) A conformance-empowered reachability analysis is developed to enhance the reliability of the PDI-driven dynamic verification. Extensive case studies demonstrate the efficacy of the ODE-Net-enabled method in microgrid dynamic model discovery, and the effectiveness of the Neuro-Reachability approach in verifying the NMs dynamics under multiple uncertainties and various operational scenarios.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 13 Jan 2021.
Builds on
6 of 28 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2016Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive controlcited by 134
- 2019NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Datacited by 42
- 2018On Robust Computation of Koopman Operator and Prediction in Random Dynamical Systemscited by 8
- 2020Enhanced Microgrid Power Flow Incorporating Hierarchical Controlcited by 2
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.
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