# Neuro-Reachability of Networked Microgrids

arXiv: https://arxiv.org/abs/2101.05159 · HTML: https://mlsanity.com/paper/2101.05159/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control](https://mlsanity.com/paper/1611.03537/) (2016)
- [NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data](https://mlsanity.com/paper/1908.03190/) (2019)
- [On Robust Computation of Koopman Operator and Prediction in Random Dynamical Systems](https://mlsanity.com/paper/1803.08562/) (2018)
- [Enhanced Microgrid Power Flow Incorporating Hierarchical Control](https://mlsanity.com/paper/2001.03939/) (2020)

## Built on by (most cited first, 2 of 2)

- [Exploration of Artificial Intelligence-oriented Power System Dynamic Simulators](https://mlsanity.com/paper/2110.00931/) (2021)
- [Neuro-Dynamic State Estimation for Networked Microgrids](https://mlsanity.com/paper/2208.12288/) (2022)

## 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.