@inproceedings{489b3dbc27364a54912867b485c02985,
title = "Parameter Estimation of Synchronous Generator Using Neural Controlled Differential Equations",
abstract = "This paper introduces a synchronous generator modeling method based on neural controlled differential equations (neural CDEs) using online sampled data. This method begins with a fifth-order generator model, where every trainable parameter is regarded as one parameter of the fifth-order generator model. The objective is to use the real-time data to learn these parameters. A training algorithm has been formulated, and it has been shown that the combination of the proposed neural CDEs and the fifth-order model can produce desired online parameter estimations for the synchronous generator. The simulation results show that the proposed method can generate a very accurate estimation and model predictions with the mean absolute percentage error of 0.04638%.",
keywords = "digital twin, Generator dynamics, neural controlled differential equation",
author = "Zhun Yin and Hong Wang and Jiang, {Zhong Ping}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 18th IEEE International Conference on Control and Automation, ICCA 2024 ; Conference date: 18-06-2024 Through 21-06-2024",
year = "2024",
doi = "10.1109/ICCA62789.2024.10591944",
language = "English (US)",
series = "IEEE International Conference on Control and Automation, ICCA",
publisher = "IEEE Computer Society",
pages = "332--339",
booktitle = "2024 IEEE 18th International Conference on Control and Automation, ICCA 2024",
}