# Neuro-Dynamic State Estimation for Networked Microgrids

arXiv: https://arxiv.org/abs/2208.12288 · HTML: https://mlsanity.com/paper/2208.12288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 25 Aug 2022.

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

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering](https://mlsanity.com/paper/1712.01317/) (2017)
- [Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed Averaging](https://mlsanity.com/paper/1504.06784/) (2015)
- [Enhanced Microgrid Power Flow Incorporating Hierarchical Control](https://mlsanity.com/paper/2001.03939/) (2020)
- [Neuro-Reachability of Networked Microgrids](https://mlsanity.com/paper/2101.05159/) (2021)

## Abstract (arXiv)

We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems. Our contributions include: 1) a data-driven Neuro-DSE algorithm for NMs DSE with partially unidentified dynamic models, which incorporates the neural-ordinary-differential-equations (ODE-Net) into Kalman filters; 2) a self-refining Neuro-DSE algorithm (Neuro-DSE+) which enables data-driven DSE under limited and noisy measurements by establishing an automatic filtering, augmenting and correcting framework; 3) a Neuro-KalmanNet-DSE algorithm which further integrates KalmanNet with Neuro-DSE to relieve the model mismatch of both neural- and physics-based dynamic models; and 4) an augmented Neuro-DSE for joint estimation of NMs states and unknown parameters (e.g., inertia). Extensive case studies demonstrate the efficacy of Neuro-DSE and its variants under different noise levels, control modes, power sources, observabilities and model knowledge, respectively.