Neuro-Dynamic State Estimation for Networked Microgrids

Fei Feng

doi:10.1109/TIA.2024.3371956

2025-01-01

Fei Feng

Yifan Zhou *

Peng Zhang

DOI：10.1109/TIA.2024.3371956

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摘要

En 中文

The increasing integration of distributed energy resources (DERs) brings complicated dynamics in networked microgrids (NMs), calling for high-fidelity dynamic state estimation (DSE) of NMs. Traditional DSE, which requires accurate physical models of the entire NMs, is becoming increasingly unattainable. This paper devises neuro-dynamic state estimation (Neuro-DSE), a learning-based DSE algorithm to track the dynamics of inverter-interfaced NMs with unknown subsystems. The process and contributions include: 1) a data-driven Neuro-DSE algorithm is established for NMs with partially unidentified dynamic models by incorporating the neural-ordinary-differential-equations (ODE-Net) into Kalman filters; 2) a self-refined Neuro-DSE$<^>{+}$ method is devised to tackle limited and noisy measurements. Specifically, Kalman filters are embedded into ODE-Net training for automatic filtering, augmenting, and correcting effects; 3) a Neuro-KalmanNet-DSE algorithm is derived to relieve the model mismatch scenarios by integrating KalmanNet with Neuro-DSE. Numerical simulations carried out on typical four-microgrid NMs reveal that Neuro-DSE can track the dynamics under various control modes (e.g., droop/secondary controls) and components. Its variants increase the accuracy of Neuro-DSE under limited measurement and model mismatch scenarios.

Keyword:

Heuristic algorithms

Power system dynamics

Kalman filters

Physics

State estimation

Noise measurement

Current measurement

Networked microgrids

neuro-dynamic state estimation

Kalman filter

neural ordinary differential equations

KalmanNet

期刊

IEEE Transactions on Industry Applications

IF:

4.5

论文数:

1.1W

被引数:

3.5W

机构

state university of new york (suny) system

学者数:

6.5W

论文数: 5.8W

被引数: 65

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