Simulation and study of maximum power point tracking for rim-driven tidal current energy power generation systems

被引：0
|
作者：
Ouyang, Yani [1 ,2 ]
Zhao, Wei [1 ]
Wang, Haifeng [1 ]
机构：
[1] Chinese Acad Sci, Inst Elect Engn, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Beijing, Peoples R China
来源：
ENERGY REPORTS | 2023年 / 9卷
基金：
中国国家自然科学基金;
关键词：
Tidal current energy power generation systems; Rim-driven; MPPT; Q-learning;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This paper presents a maximum power point tracking (MPPT) control algorithm based on an intelligent reinforcement learning. The proposed model-free Q-learning algorithm realizes the online learning of the control algorithm of the tidal power generation system by updating the action values stored in the Q-table. By learning the optimal rotor speed-output power curve, the algorithm fits the optimal generator curve and applies the optimal P-e -omega(r) curve to the optimal control method of the tidal power generation systems. (c) 2023 Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码：792 / 801
页数：10
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