US 12,460,619 B2
System and method for optimizing wind farm performance using unmanned aerial vehicles
Scott Charles Evans, Burnt Hills, NY (US); Samual Bryan Shartzer, Greenville, SC (US); Stefan Kern, Munich (DE); Tapan Ravin Shah, Los Altos, CA (US); and Anveshi Charuvaka, Tracy, CA (US)
Assigned to GE Vernova Infrastructure Technology LLC, Greenville, SC (US)
Filed by General Electric Renovables Espana, S.L., Barcelona (ES)
Filed on Jan. 26, 2023, as Appl. No. 18/159,706.
Prior Publication US 2024/0254966 A1, Aug. 1, 2024
Int. Cl. F03D 7/04 (2006.01); B64U 20/80 (2023.01); B64U 101/35 (2023.01); F03D 7/02 (2006.01)
CPC F03D 7/048 (2013.01) [B64U 20/80 (2023.01); F03D 7/0224 (2013.01); F03D 7/0276 (2013.01); F03D 7/045 (2013.01); F03D 7/046 (2013.01); B64U 2101/35 (2023.01); F05B 2270/32 (2013.01); F05B 2270/321 (2013.01); F05B 2270/323 (2013.01); F05B 2270/325 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method for optimizing performance of a wind farm having at least one wind turbine, the method comprising:
maneuvering a first unmanned aerial vehicle (UAV) having at least one sensor to a first location near the at least one wind turbine of the wind farm;
collecting, via the at least one sensor of the first UAV, data corresponding to one or more wind conditions at the at least one wind turbine;
receiving the data corresponding to the one or more wind conditions at the at least one wind turbine via a controller;
generating, via a machine learning algorithm programmed in the controller, a control action for the at least one wind turbine using the data corresponding to the one or more wind condition at the at least one wind turbine;
implementing, via the controller, the control action;
generating, via the machine learning algorithm, a prioritized list of desired data that instructs a second location to retrieve the data, wherein the second location comprises an area with the greatest uncertainty and highest value for use in improving the machine learning algorithm; and
maneuvering the first UAV to the second location.