Wind turbines can be clustered while avoiding turbulent wakes of their neighbors
The angles of wind turbines relative to the horizon -- their yaw -- must continually adjust to prevent their wakes from disrupting the efficiency of their neighbors. File Photo by Pat Benic/UPI | License Photo
July 20 (UPI) -- Clustering wind turbines can help power producers generate more electricity while taking up less space. Unfortunately, the air turbulence created by a spinning wind turbine -- sometimes called its wake -- can disrupt the efficiency of the turbine's neighbors.
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.
One of the tools that can be used to optimize an individual turbine's power production is its yaw, the turbine's angle relative to the horizontal plane. A turbine's yaw can also be adjusted to reposition its wake away from downwind neighbors.
Unfortunately, wind conditions frequently change. To keep turbines optimized and wake-free, yaws must be adjusted as the wind changes.
For the new study, scientists showed how data already being collected by turbine sensors can be used to inform yaw adjustment. Models suggest the automatic yaw adjustment system can boost a wind farm's power production by 1 percent.
If adopted industry-wide, such an increase in production would translate to 3 billion kilowatts per year.
"The exciting part about our work is that it matches reality, impacting real people," said study co-author Federico Bernardoni. "Operators can use these results to identify when they should apply yaw control, and to which group, to maximize wind power gain."
The data analysis system, however, is not a model, as it makes no assumptions about environmental conditions. The system works by analyzing data fielded directly from the individual turbines that make up 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%."
One of the tools that can be used to optimize an individual turbine's power production is its yaw, the turbine's angle relative to the horizontal plane. A turbine's yaw can also be adjusted to reposition its wake away from downwind neighbors.
Unfortunately, wind conditions frequently change. To keep turbines optimized and wake-free, yaws must be adjusted as the wind changes.
For the new study, scientists showed how data already being collected by turbine sensors can be used to inform yaw adjustment. Models suggest the automatic yaw adjustment system can boost a wind farm's power production by 1 percent.
If adopted industry-wide, such an increase in production would translate to 3 billion kilowatts per year.
"The exciting part about our work is that it matches reality, impacting real people," said study co-author Federico Bernardoni. "Operators can use these results to identify when they should apply yaw control, and to which group, to maximize wind power gain."
The data analysis system, however, is not a model, as it makes no assumptions about environmental conditions. The system works by analyzing data fielded directly from the individual turbines that make up 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%."
Data identifies turbine wake clustering, improves wind farm productivity via yaw control
Truly green energy by seeing the forest despite the trees
IMAGE: YELLOW AREAS INDICATE LOW VELOCITY WAKES THAT EXTEND DOWNSTREAM OF WIND TURBINES, AND THE ALGORITHM IDENTIFIES CLUSTERS OF TURBINES (REPRESENTED BY COLORED RECTANGLES) THAT CAN BE OPTIMIZED AS A GROUP... view more
CREDIT: UNIVERSITY OF TEXAS AT DALLAS
WASHINGTON, July 20, 2021 -- In the wind power industry, optimization of yaw, the alignment of a wind turbine's angle relative to the horizonal plane, has long shown promise for mitigating wake effects that cause a downstream turbine to produce less power than its upstream partner. However, a critical missing puzzle piece in the application of this knowledge has recently been added -- how to automate the identification of which turbines are experiencing wake effects amid changing wind conditions.
In the Journal of Renewable and Sustainable Energy, by AIP Publishing, researchers from the University of Texas at Dallas describe a real-time method for potentially helping turbine farms realize additional power from the clustering of their turbines. Their method requires no new sensors to identify which turbines at any given time could increase power production if yaw control is applied, and validation studies showed an increase of 1%-3% in overall power gain.
"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," said co-author Stefano Leonardi. "This is what we solved. This is our contribution."
Wind farms consist of multiple turbines built close together, each converting kinetic energy into electricity. Optimizing power production from an individual turbine depends on many factors (e.g., stratification, temperature, turbulence, topography, etc.), but optimizing production of the farm as a whole also involves interactions between turbines. A downstream turbine in the wake of another encounters decreased wind, reducing turbine power production up to 60%.
The researchers identified how to create clusters or links between turbines by identifying correlations in data currently collected by turbine sensors. Wind farm owners can then use this automated information to guide employment of a standard procedure for yaw control, based on the past decade of studies about yaw optimization. Each 1% increase in energy production would represent 3 billion kilowatts per year.
"The exciting part about our work is that it matches reality, impacting real people," said co-author Federico Bernardoni. "Operators can use these results to identify when they should apply yaw control, and to which group, to maximize wind power gain."
Since the turbines already have the hardware and sensors, and the land is already committed to the wind farm, any increase in power production using this method would be truly green energy. The method is also unique because it is model-free. It makes no assumptions about current parameters or conditions, minimizing the effects of uncertainty present in current wake models.
"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[%]-3%."
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The article "Identification of wind turbine clusters for effective real time yaw control optimization" is authored by Stefano Leonardi, Federico Bernardoni, Umberto Ciri, and Mario Rote. The article will appear in Journal of Renewable and Sustainable Energy on July 20, 2021 (DOI: 10.1063/5.0036640). After that date, it can be accessed at https:/
ABOUT THE JOURNAL
Journal of Renewable and Sustainable Energy is an interdisciplinary journal that publishes across all areas of renewable and sustainable energy relevant to the physical science and engineering communities. Topics covered include solar, wind, biofuels and more, as well as renewable energy integration, energy meteorology and climatology, and renewable resourcing and forecasting. See https:/
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