According to the researchers, the new method has the potential to generate $600m in added wind power in the US.

Several years ago, UT Dallas mechanical engineering associate professor Stefano Leonardi and his team created models capable of integrating physical behavior across a wide range of length scales from 100m-long turbine rotors to centimeters-thick tips of blades. The models are intended to predict wind power with accuracy using supercomputers.

Leonardi said: “We developed a code to mimic wind turbines, taking into account the interference between the wake of the tower and the nacelle [the cover that houses all of the generating components in a wind turbine] with the wake of the turbine rotor.”

However, in order to model the variability of wind for a given region at a specific time, the team integrated their code with the Weather Research and Forecasting Model (WRF), a weather prediction model developed at the National Center for Atmospheric Research.

Leonardi added: “We can get the wind field from the North American Mesoscale Model on a coarse grid, use it as an input for five nested domains with progressively higher resolution and reproduce with high fidelity the power generation of a real wind farm.”

The new researches have tested their control algorithms using their modeling capabilities to manage the operation of dynamic systems at wind farms.

The control algorithms used for testing is the extremum seeking control, which is developed as a model-free way of getting the best performance out of dynamic systems when only limited knowledge of the system is known.

As part of the testing, the team conducted virtual wind experiments using supercomputers, including Stampede2 and Lonestar 5, at the TACC.

UT Dallas mechanical engineering professor Mario Rotea said: “The benefits of using high performance computing to create a virtual platform for doing analyses of proposed solutions for wind energy are enormous.

“The more we can do with computers, the less we have to do with testing, which is a big part of the costs. This benefits the nation by lowering the cost of energy.”

The researchers are due to carry out field testing to validate the application of extremum seeking control to wind farms.

The team, however, applied the method to a single turbine at the National Renewable Energy Laboratory (NREL).

Rotea said: “The NREL test gave us experimental data supporting the value of extremum seeking control for wind power maximization.

“The experimental results show that extremum seeking control increases the power capture by 8-12% relative to a baseline controller.”

The UT Dallas team is now planning to undertake an experimental campaign involving a cluster of turbines in a wind farm.