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.
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Data identifies turbine wake clustering, improves wind farm productivity via yaw control
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