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.
@article{arxiv.2208.12288,
title = {Neuro-Dynamic State Estimation for Networked Microgrids},
author = {Fei Feng and Yifan Zhou and Peng Zhang},
journal= {arXiv preprint arXiv:2208.12288},
year = {2023}
}
Comments
This paper needs to be withdrawn by the author. In Section II, Part C, there is lack of procedure to achieve parameter estimation using the proposed model. In Section V, Part E, experiment parameter setting is missed. Noise for estimating inertia case needs to be reset for simulation. Additional tests need to be added. These two parts need to be rewritten