Wind farms that are clustered together can generate more electricity while taking up less space. Unfortunately, the air turbulence caused by a spinning wind turbine, known as the rotor’s wake, can reduce the efficiency of its neighbours.
According to a new study, published Tuesday in the journal of Renewable and Sustainable Energy, scientists have developed a new data analysis method to help wind farm operators adjust the yaw of clustered turbines to avoid interference.
Simulations showed the new method, which requires no new sensors, can provide a 1% to 3% boost of energy.
“There was a huge gap in how to determine, automatically, which turbine is in the wake of another in the field with variable wind conditions,” co-author Stefano Leonardi said in a press release.
“This is what we solved. This is our contribution,” said Leonardi, a researcher with the Center for Wind Energy at the University of Texas at Dallas.
When building and managing wind farms, operators must consider a variety of factors, such as topography and temperature, when optimizing the energy production of each individual turbine. But engineers must also consider how each turbine effects those around them.
The wake from an upwind turbine can reduce the power production of a turbine downwind by as much as 60 percent.
The yaw, or angle of the turbine relative to the horizontal plane, is one of the techniques that can be used to improve an individual turbine’s power production. The yaw of a turbine can also be changed to move its wake away from downwind neighbours.
Wind conditions fluctuate regularly, which is unfortunate. Yaws must be changed as the wind changes to maintain turbines optimal and wake-free.
Scientists demonstrated how data acquired by turbine sensors might be used to advise yaw adjustment in the new study. According to models, the automatic yaw adjustment technology can increase a wind farm’s power output by 1%.
If implemented across the sector, this increase in production would amount to 3 billion kilowatts per year.
“What excites me about our work is that it reflects reality and has an influence on actual individuals,” study co-author Federico Bernardoni said. “Operators can utilise these findings to determine when and to which groups they should apply yaw control to optimum wind power gain.”
The data analysis system, on the other hand, is not a model because it makes no assumptions about the environment. The method operates by analysing data collected directly from individual wind turbines in a wind farm.
“By just making turbines smarter, we’re getting more energy from something that already exists,” said Leonardi. “Using just simple math, we’re increasing energy, so that’s a very clean, green 1% to 3%.”