Deep Reinforcement Learning-Based Control Scheme for Performance Enhancement of PMSG Wind Turbine With Vienna Rectifier

A novel control scheme based on deep reinforcement learning (DRL) is presented to improve the operational performance of a permanent magnet synchronous generator (PMSG) with a Vienna rectifier (PGVR) in a wind turbine generator system. This article investigates the harmonics problem of stator curren...

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Veröffentlicht in:	IEEE journal of emerging and selected topics in power electronics Jg. 13; H. 1; S. 432 - 444
Hauptverfasser:	Du, Yucheng, Cai, Bin, Yan, Shaomin, Zhang, Weiyu, Xing, Zida, Qiu, Yalan, Chu, Xiaoguang, Song, Xiaoyu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Control systems Deep learning Deep reinforcement learning (DRL) Fluctuations Harmonic analysis Performance enhancement permanent magnet synchronous generator (PMSG) Permanent magnets Rectifiers Stators Synchronous machines Task analysis Turbogenerators twin delay deep deterministic policy gradient (TD3) Vienna rectifier Wind speed Wind turbines
ISSN:	2168-6777, 2168-6785
Online-Zugang:	Volltext
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