Deep Reinforcement Learning for Wind-Power: An Overview

In this scientific review paper, we examine the recent advancements in using Deep Reinforcement Learning (DRL) for control and optimization in the area of wind power, with a specific emphasis on its application in controlling wind turbines. To start, we provide an introduction to the fundamental concepts of DRL and its crucial components. Then, we delve into the existing research on using DRL for the control of wind turbines, including the use of DRL for tracking the maximum power point and maximizing power generation. Finally, we explore the difficulties and future perspectives of utilizing DRL in the wind power sector, including its integration with traditional control methods and scalability to large wind farm systems. Our review highlights the immense potential of DRL as a tool for optimizing wind power systems and offers a guide for future research in this field