US 12,331,720 B2
Wind turbine replacement schedule
Juergen Herp, Odense M (DK); Niels Lovmand Pedersen, Gedved (DK); and Benedikt Wiese, Odense C (DK)
Assigned to SIEMENS GAMESA RENEWABLE ENERGY A/S, Brande (DK)
Appl. No. 17/613,534
Filed by Siemens Gamesa Renewable Energy A/S, Brande (DK)
PCT Filed Jun. 2, 2020, PCT No. PCT/EP2020/065188
§ 371(c)(1), (2) Date Nov. 23, 2021,
PCT Pub. No. WO2020/249424, PCT Pub. Date Dec. 17, 2020.
Claims priority of application No. 19180276 (EP), filed on Jun. 14, 2019.
Prior Publication US 2022/0252050 A1, Aug. 11, 2022
Int. Cl. F03D 17/00 (2016.01); F03D 7/02 (2006.01)
CPC F03D 17/00 (2016.05) [F03D 7/0292 (2013.01); F05B 2260/821 (2013.01); F05B 2270/32 (2013.01); F05B 2270/334 (2013.01); F05B 2270/335 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method of estimating replacement schedule of a mechanical component of a wind turbine, the method comprising:
detecting a value of a physical quantity indicating a malfunction of the mechanical component of the wind turbine;
estimating remaining producible energy until the mechanical component is to be replaced, wherein estimating is performed when the value of the physical quantity exceeds a threshold; and
operating the wind turbine for optimizing power output based on the estimated remaining producible energy;
wherein estimating the remaining producible energy uses machine learning and includes using a neural network which has been trained by training data from plural training wind turbines, the training data comprising training values of input parameters and a training value of at least one output parameter, the input parameters comprising at least one wind turbine operational parameter and/or at least one ambient condition parameter, the at least one output parameter being indicative for the remaining producible energy until the mechanical component is to be replaced.