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

作　　者：Mahdi Abkar Navid Zehtabiyan-Rezaie Alexandros Iosifidis 

机构地区：[1]Department of Mechanical and Production Engineering,Aarhus University,Aarhus N,8200,Denmark [2]Department of Electrical and Computer Engineering,Aarhus University,Aarhus N,8200,Denmark [3]Centre for Digitalization,Big Data,and Data Analytics,Aarhus University,Aarhus N,8200,Denmark

出　　处：《Theoretical & Applied Mechanics Letters》2023年第6期455-464,共10页力学快报（英文版）

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

摘　　要：Wind-farm flow control stands at the forefront of grand challenges in wind-energy science.The 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 interactions.Reinforcement 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 control.This review has two main objectives.Firstly,it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL methods.By 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 techniques.Secondly,it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on RL.By highlighting these challenges,the review aims to identify areas requiring further exploration and potential opportunities for future research.

关 键 词：Wind-farm flow control Turbine wakes Power losses Reinforcement learning Machine learning 

分 类 号：TM614[电气工程—电力系统及自动化] TP181[自动化与计算机技术—控制理论与控制工程]