Wind farm control technologies: from classical control to reinforcement learning

Abstract

Abstract Wind power plays a vital role in the global effort towards net zero. A recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. The control system is the core of wind farm operations and has an essential influence on the farm’s power capture efficiency, economic profitability, and operation and maintenance cost. However, the inherent system complexities of wind farms and the aerodynamic interactions among wind turbines cause significant barriers to control system design. The wind industry has recognized that new technologies are needed to handle wind farm control tasks, especially for large-scale offshore wind farms. This paper provides a comprehensive review of the development and most recent advances in wind farm control technologies. It covers the introduction of fundamental aspects of wind farm control in terms of system modeling, main challenges and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue load minimization and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and similarities between model-based, model-free and data-driven wind farm approaches is presented. In addition, we highlight state-of-the-art wind farm control technologies based on reinforcement learning—a booming machine learning technique that has drawn worldwide attention. Future challenges and research avenues in wind farm control are also analyzed.