US 12,110,867 B2
Wind turbine control based on reinforcement learning
Bert Gollnick, Hamburg (DE)
Assigned to Siemens Gamesa Renewable Energy A/S, Brande (DK)
Appl. No. 17/639,925
Filed by Siemens Gamesa Renewable Energy A/S, Brande (DK)
PCT Filed Aug. 13, 2020, PCT No. PCT/EP2020/072731
§ 371(c)(1), (2) Date Mar. 3, 2022,
PCT Pub. No. WO2021/052686, PCT Pub. Date Mar. 25, 2021.
Claims priority of application No. 19197556 (EP), filed on Sep. 16, 2019.
Prior Publication US 2022/0325696 A1, Oct. 13, 2022
Int. Cl. F03D 7/04 (2006.01)
CPC F03D 7/046 (2013.01) [F05B 2270/404 (2013.01); F05B 2270/709 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A method of controlling a wind turbine, comprising:
receiving data indicative of a current environmental state of the wind turbine, wherein the current environmental state of the wind turbine specifies a plurality of the following: a wind speed, a turbulence intensity, a site elevation, a temperature, a vertical wind shear, or a horizontal wind shear;
determining one or more controlling actions of the wind turbine based on the current environmental state of the wind turbine and a reinforcement learning algorithm, wherein the one or more controlling actions comprise at least one of keep-idling or start-up;
predicting a reward value for a future point in time for possible chains of controlling actions selected from a predefined set of controlling actions, respectively, by processing the current environmental state together with a chain of controlling actions using a predefined value function of the reinforcement learning algorithm, wherein the one or more chains of controlling actions are determined based on a highest predicted reward value for the future point in time for the chain of controlling actions; and
controlling the wind turbine by applying the one or more controlling actions to the wind turbine.