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Physics-Informed, Safety and Stability Certified Neural Control for Uncertain Networked Microgrids
- Source :
- IEEE Transactions on Smart Grid; January 2024, Vol. 15 Issue: 1 p1184-1187, 4p
- Publication Year :
- 2024
-
Abstract
- This letter devises a physics-informed neural hierarchical control for uncertain networked microgrids (NMs) to provide certificated safe and stable control of NMs undergoing disturbances and uncertain perturbations. The main contributions include 1) a learning-based hierarchical control framework for inverter-based resources (IBRs) in NMs under unprecedented uncertainties of renewable energies; 2) a robust control Lyapunov barrier function (rCLBF) to provide provable safety and stability guarantees under uncertain scenarios; 3) an rCLBF-based, physics-informed learning scheme to simultaneously discover the certificates and control policy with explicit safety, stability, and robustness guarantees, enabling certified generalization beyond nominal operating scenarios. The efficacy of the rCLBF-based neural hierarchical control is thoroughly validated in different NMs cases.
Details
- Language :
- English
- ISSN :
- 19493053
- Volume :
- 15
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Smart Grid
- Publication Type :
- Periodical
- Accession number :
- ejs65035097
- Full Text :
- https://doi.org/10.1109/TSG.2023.3309534