Wind farm control technologies : from classical control to reinforcement learning

Wind power plays a vital role in the global effort towards net zero. The recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. Control system is the core in wind farm operations and has essential influences on the farm’s power capture efficiency, economic profitability, and operation & maintenance cost. However, wind farms’ inherent system complexities and the aerodynamic interactions among wind turbines bring significant barriers to control systems 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 of wind farm control technologies. This covers the introduction of fundamental aspects in wind farm control in terms of system modelling, main challenges, and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue loads minimization, and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and connections among model-based, model-free and data-driven wind farm approaches is presented. In addition, highlights are made on the 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 analysed