@inproceedings{71eef5fb6477447ba5f4684b23595cdd,
title = "A New Reinforcement Learning Based Voltage Control for Three-Phase AC/DC Converter",
abstract = "This paper proposes a novel integral reinforcement learning (IRL) based DC-link voltage control method for three-phase AC/DC converter. The proposed IRL control autonomously updates the optimal control gains using online data trained neural network. It produces superior control performance without the knowledge of system model parameters. Compared with the existing control approaches for three-phase AC/DC converter, it offers the benefits of model independence and autonomous gain tuning. The effectiveness of the proposed IRL method is verified by simulation results.",
keywords = "model dependence, reinforcement learning, three-phase AC/DC converter, voltage control",
author = "Tianhao Qie and Xinan Zhang and Herbert Iu and Tyrone Fernando",
note = "Funding Information: The first author of this paper is a Higher Degree Researcher in The University of Western Australia - led microgrid battery deployment project, funded by the Future Battery Industries Cooperative Research Centre as part of the Australian Government Cooperative Research Centres Program Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2023 ; Conference date: 14-07-2023 Through 16-07-2023",
year = "2023",
month = sep,
day = "5",
doi = "10.1109/ICPICS58376.2023.10235709",
language = "English",
series = "2023 IEEE 5th International Conference on Power, Intelligent Computing and Systems, ICPICS 2023",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "152--156",
booktitle = "2023 IEEE 5th International Conference on Power, Intelligent Computing and Systems, ICPICS 2023",
address = "United States",
}