A Novel Integral Reinforcement Learning-Based Control Method Assisted by Twin Delayed Deep Deterministic Policy Gradient for Solid Oxide Fuel Cell in DC Microgrid

This paper proposes a new online integral reinforcement learning (IRL)-based control algorithm for the solid oxide fuel cell (SOFC) to overcome the long-lasting problems of model dependency and sensitivity to offline training dataset in the existing SOFC control approaches. The proposed method autom...

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Veröffentlicht in:	IEEE transactions on sustainable energy Jg. 14; H. 1; S. 1 - 16
Hauptverfasser:	Liu, Yulin, Qie, Tianhao, Yu, Yang, Wang, Yuxuan, Chau, Tat Kei, Zhang, Xinan, Manandhar, Ujjal, Li, Sinan, Iu, Herbert, Fernando, Tyrone
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computational modeling Computer applications Control methods Control theory DC Microgrid Distributed generation Fuel cells Fuel technology Hardware-In-the-Loop Integral Reinforcement Learning Learning Machine learning Mathematical models Microgrids Neural networks Optimal control Solid Oxide Fuel Cell Solid oxide fuel cells Training Tuning Twin Delayed Deep Deterministic Policy Gradient Voltage control
ISSN:	1949-3029, 1949-3037
Online-Zugang:	Volltext
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