Wind turbine control based on reinforcement learning
AssigneeSiemens Gamesa Renewable Energy A/S
InventorBert Gollnick
Life of this patent
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Abstract
Methods, systems, and devices for wind turbine control based on reinforcement learning are disclosed. The method comprises receiving data indicative of a current environmental state of the wind turbine, 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, and applying the determined one or more controlling actions to the wind turbine.
Claims12
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.
A device for controlling a wind turbine, the device comprising one or more processors, the one or more processors being configured to:
- receive 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;
- determine 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;
- predict 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
- control the wind turbine by applying one or more controlling actions to the wind turbine.
Dependencies are inferred from the claim text.