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More power, less wear-and-tear − new control methods to optimize wind farm performance

Wind turbines: It’s a group effort

Modeling the interactions between turbines in a wind tunnel.
Modeling the interactions between turbines in a wind tunnel. (Photo: Carlo L. Botasso / TUM)

Research news

Often hundreds of rotors can be installed in a typical wind farm. A little known fact, however, is that the shadowing caused by the wind turbine rotors impacts the performance of neighboring turbines and reduces their lifespan. As part of an EU project, researchers from the Technical University of Munich (TUM) are studying these interactions in a wind tunnel and developing a computer model that will help improve the efficiency of wind farms.

"Up to now, wind turbines have tended to be solo performers, with each installation designed to deliver maximum power over the longest possible period of time," explains Johannes Schreiber, research associate at the Chair of Wind Energy. This "individualistic" perspective is not conducive to the overall performance of the wind farm, however, according to Schreiber: "Each turbine creates wind shadow and turbulence, which may have a negative influence on its neighboring installations. If the objective is to make wind farms work as efficiently as possible, it is vital to focus on the collective operation of the wind farm, instead of the individual behavior of each wind turbine."

Tapping more power with intelligent control

This presents quite a challenge for scientists, since the complex interactions within a wind farm are hard to gauge and even harder to control. Each rotor influences the movement of air that flows around it, slowing it down and creating turbulence. This shadow effect negatively impacts the power generation of the downstream turbines and stresses their components. With tower, rotor blades and generators constantly exposed to turbulent air flows, premature fatigue is inevitable. Scientists from around the world have been trying for years to make wind farms more efficient by minimizing the impact of wind shadow.

The TUM researchers have now shown in experimental tests, conducted within the German-funded CompactWind project, that intelligent control technologies can reduce shadow effects in wind farms: "This approach involves deflecting the wake – the turbulent and low speed shadow shed by each wind turbine – so that it does not directly impact the downstream machines," explains Prof. Carlo Bottasso, Chair of Wind Energy at TUM. Last fall, Prof. Bottasso and his team were awarded the Bavarian Energy Prize for their work on wind farm control.

Putting it to the test in a wind tunnel

But how do you go about optimizing the air flow in a wind farm? "The wake can be deflected by yawing the rotor, which means that the wind hits the rotor slightly from the side rather than head-on," clarifies Schreiber.

In order to better understand how the different ways of operating wind turbines influence the power yield of wind farms as a whole, the TUM team built scale models. Like their full-scale counterparts, the 1.5-meter miniature turbines feature generators, drives and even motors to adjust the individual blades, exactly as real wind turbines. Each model is also equipped with sensors to determine the forces affecting its various components.

With three of these models in their luggage, the researchers set off from Munich to Milan. The Politecnico di Milano has a wind tunnel capable of replicating to scale the air flows in the lower atmosphere of the Earth. "Our tests confirmed that wake interactions do indeed reduce power yield," reports Schreiber.

The next step for the engineers was to find a way to optimize power output. They connected the three models to a central controller, also developed by the Chair of Wind Energy, which operates as the "brain" of the wind farm. This control system ensures that the wind turbines work as a group to deliver the best possible outcome. This is in contrast to the current practice, where each wind turbine operates individually, without any coordination with its fellow wind turbines.

During the experiments, the upwind turbines were automatically yawed to point slightly away from the wind in order to reduce the shading on the downstream turbines. While this reduces the performance of the yawed wind turbine, this loss is more than compensated for by the greater power generated by the downstream turbines. With the wake deflected, the components of these turbines are also exposed to less fatigue, resulting in an extended life. "Our measurements in the wind tunnel showed for the first time that automated wind farm control capabilities can significantly improve power output," concludes Bottasso.

More renewable energy at less cost

The team will now contribute its know-how to the EU’s "Closed Loop Wind Farm Control (CL-WINDCON)" project. As part of "CL-WINDCON", the TUM researchers will develop and test an intelligent control system for wind farms in collaboration with 13 partners from six countries.

The aim for the scientists over the next three years is to optimize the automated control of wind farms and follow this up with full-scale field tests. The ultimate aim is to achieve more power at less cost. In other words, wind turbines of the future should be efficiently managed so that they collectively maximize wind farm power while lowering maintenance costs.


The TUM researchers’ work on the CompactWind project was financed by the German Federal Ministry for Economic Affairs and Energy (BMWi).

More information:

Short portait of the project on Youtube


Journal of Physics: Conference Series, Volume 753, B. Wind, wakes, turbulence and wind farms: "Wind tunnel testing of a closed-loop wake deflection controller for wind farm power maximization", Filippo Campagnolo, Vlaho Petrović, Johannes Schreiber, Emmanouil M. Nanos, Alessandro Croce and Carlo L. Bottasso

High-resolution picture:



Johannes Schreiber, M.Sc.
Technical University of Munich

Tel.: +49 (0)89 289 16683


Dr. Filippo Campagnolo

Technical University of Munich

Tel.: +49 (0)89 289 16684


Prof. Dr. Carlo L. Bottasso
Technical University of Munich

Tel.: +49 (0)89 289 16680