@inproceedings{07fa4520a23449558ad980386306e5b9,
title = "A Physics-informed Neural Network Method for LC Parameter Estimation in Three-Phase Inverter",
abstract = "The DC-link capacitance and the AC-side inductance parameters can be used as feedback for control optimization and component degradation monitoring. This paper proposes a parameter estimation method based on the combination use of artificial neural network and circuit analytical models, e.g., physics-informed neural network (PINN), for a three-phase inverter application. It does not require any additional hardware circuitry and can be well-trained based on a small training dataset. A three-phase inverter case study is presented with theoretical analyses, simulations, and experimental verifications. The results show that satisfactory accuracy can be achieved for the estimation of DC-link capacitance and AC-side inductance parameters.",
keywords = "Parameter estimation, capacitor, condition monitoring, inductor, physics-informed neural network, three-phase inverter",
author = "Jie Kong and Dao Zhou and Xing Wei and Huai Wang",
year = "2024",
month = jul,
day = "2",
doi = "10.1109/IPEMC-ECCEAsia60879.2024.10567996",
language = "English",
isbn = "979-8-3503-5134-7",
series = "International Power Electronics and Motion Control Conference (PEMC)",
publisher = "IEEE (Institute of Electrical and Electronics Engineers)",
pages = "3957--3962",
booktitle = "2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia)",
address = "United States",
note = "2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia) ; Conference date: 17-05-2024 Through 20-05-2024",
}