Reinforcement learning for wind-farm flow control:Current state and future actions

作     者：Mahdi Abkar Navid Zehtabiyan-Rezaie Alexandros Iosifidis Mahdi Abkar;Navid Zehtabiyan-Rezaie;Alexandros Iosifidis

作者机构：Department of Mechanical and Production EngineeringAarhus UniversityAarhus N8200Denmark Department of Electrical and Computer EngineeringAarhus UniversityAarhus N8200Denmark Centre for DigitalizationBig Dataand Data AnalyticsAarhus UniversityAarhus N8200Denmark 

出 版 物：《Theoretical & Applied Mechanics Letters》 (力学快报（英文版）)

年 卷 期：2023年第13卷第6期

页      面：455-464页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0807[工学-动力工程及工程热物理] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the financial support from the Independent Research Fund Denmark(DFF)under Grant No.0217-00038B 

主　　题：Wind-farm flow control Turbine wakes Power losses Reinforcement learning Machine learning 

摘      要：Wind-farm flow control stands at the forefront of grand challenges in wind-energy *** central issue is that current algorithms are based on simplified models and,thus,fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere *** learning(RL),as a subset of machine learning,has demonstrated its effectiveness in solving high-dimensional problems in various domains,and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow *** review has two main ***,it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL *** examining the latest research in this area,the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL ***,it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on *** highlighting these challenges,the review aims to identify areas requiring further exploration and potential opportunities for future research.