When wind travels through a turbine, it creates a wake that reduces the average Wind Velocity downstream. The more significant the impact on the downstream wake profile, the quicker the turbine blades spin about the wind speed. Upstream turbines must be efficiently controlled in wind farms to avoid adversely affected by upstream wake effects.
Researchers from the University of Illinois at Urbana-Champaign report in the Journal of Renewable and Sustainable Energy that building controllers based on understanding the wind farm system as a coupled network make it possible to harvest power more effectively.Author Lucas Buccafusca said, “If you think of a wind farm as a group of turbines each vying for the incoming wind, if every turbine is greedy and tries to maximize its own power, the system as a whole is suboptimal. Our work seeks to design controls for turbines to work collectively, thereby improving performance.”
The researchers use a model predictive control (MPC) framework for variable wind velocities and wake steering approaches to show that adding these strategies into future wind turbine control algorithms could be beneficial. Wind blowing through upstream turbines causes turbulence and power spikes, which the researchers hope to reduce.
The researchers discovered that using control algorithms that incorporate downstream effects improves performance significantly. Axial induction and yaw misalignment controls were used to assign turbine controls, and the results were validated using wake steering models. The researchers want to see if they can apply similar techniques to a distributed wind turbine energy problem. Each turbine has its local battery that stores surplus energy. When supply is inadequate, such as when Wind Velocity is insufficient to fulfill the grid operator’s demand, the battery can return that energy.