@inproceedings{e920f81a297943078b859b7ae75642b1,
title = "A Novel Data-driven Excitation Control for the MPPT of Wound Rotor Synchronous Generator based Wind Turbine",
abstract = "This paper proposes an integral reinforcement learning (IRL)-based excitation control for the maximum power point tracking (MPPT) of wound rotor synchronous generator (WRSG)-based wind turbines. The proposed method produces superior dynamic performance over the conventional wind turbine MPPT excitation control in the presence of wind speed variations and grid disturbances. Moreover, its data-driven nature makes the proposed method independent of the system mathematical model, indicating very strong parameter robustness. Additionally, the proposed method possesses a very low computational complexity, which is desirable for real-time applications. The effectiveness of the proposed algorithm is verified by simulation results.",
keywords = "Excitation control, Integral reinforcement learning, MPPT, Wind turbine, Wound rotor synchronous generator",
author = "Tianhao Qie and Chau, {Tat Kei} and Xinan Zhang and Herbert Iu and Tyrone Fernando and Yulin Liu and Yang Yu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 31st Australasian Universities Power Engineering Conference, AUPEC 2021 ; Conference date: 26-09-2021 Through 30-09-2021",
year = "2021",
doi = "10.1109/AUPEC52110.2021.9597707",
language = "English",
series = "Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
editor = "Sumedha Rajakaruna and Siada, {Ahmed Abu} and Iu, {Ho Ching} and Arindam Ghosh and Tyrone Fernando",
booktitle = "Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021",
address = "United States",
}