In order to minimize the wind turbine’s wake effect and thereby raise the power generating efficiency of wind farms, some scientists have turned their sights towards controlling the angle of upstream turbines, to hopefully transform wind turbines into a mature industry. These scientists invented a system that can intelligently adjust the turbines’ yaw angle and pitch angle such that downstream turbines are not adversely affected by wake effects.
Almost all wind farms must contend with the issue of wake effects, which refers to a phenomena where wind passes through, is intercepted by, and transformed into kinetic energy by turbines. Once that happens, the wind in question becomes slower and weaker upon reaching downstream wind turbines, hence reducing said turbines’ efficiency and lowering the overall wind farm’s performance. In view of this, researchers from the University of Illinois Urbana-Champaign believe that approaching the development of wind farms from a coupling network design paradigm will result in more efficient wind power generation.
“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,” according to Lucas Buccafuscca, graduate student at UIUC’s Grainger College of Engineering. “Our work seeks to design controls for turbines to work collectively, thereby improving performance.”
To do this, the research team first set up a model predictive control regarding different wind speeds. Combined with the turbines’ wake steering technologies, the researchers have proven the validity of their solutions. More specifically, the wake steering technology allows the turbines to dynamically adjust their angle of their blades in order to steer the wake in a way that does not disrupt the downstream turbines. In so doing, although this mechanism somewhat decreases the electricity generated by upstream turbines, it also effectively minimizes the energy loss from wake effects on downstream turbines. “[I]t is surprising just how much the gains can be for even small wind turbine arrays simply by implementing wake steering techniques,” claimed Buccafusca.
▲“Wind turbine with yaw angle and pitch angle” (Source: AIP)
The researchers also discovered that the wind farm’s performance can be remarkably improved if the algorithms are able to take into account downstream effects. “The method for assigning turbine controls used axial induction factors and yaw misalignment controls that were shown via wake steering simulations to validate the results” (Buccafusca & Beck, 2021).
The researchers are also planning to expand the application of their solution going forward by implementing similar solutions to solve problems with distributed wind power by installing batteries in each wind turbine to store excess electricity, which can then be released to the electrical grid during low power generating periods. Although the team is currently primarily focused on tracking wind power efficiency, but their multi-target predictive and control models can also be applied to various distributive optimizations or consensus control issues.