Control of a wind turbine based on predicting amplitude modulation (AM) noise

An electronic device configured to perform an operation controlling a wind turbine, the operation comprising:

• obtaining wind turbine data associated with the wind turbine, wherein the wind turbine data is indicative of conditions of operation of the wind turbine;
• obtaining a measured Amplitude Modulation (AM) noise parameter indicative of measured AM noise in a region at a distance from the wind turbine;
• predicting an AM noise parameter indicative of AM noise within the region at the distance from the wind turbine by applying a machine learning model to the wind turbine data;
• comparing the measured AM noise parameter and the predicted AM noise parameter;
• determining, based on the comparison between the measured AM noise parameter and the predicted AM noise parameter, a source of the AM noise near the wind turbine;
• generating, based on the AM noise parameter, control data indicative of a control operation of the wind turbine, wherein the control data comprises:
• determining whether the measured AM noise parameter exceeds a first threshold;
• determining whether the comparison between the measured AM noise parameter and the predicted AM noise parameter satisfies a second threshold;
• and
• only after determining that the measured AM noise parameter exceeds the first threshold and that the comparison between the measured AM noise parameter and the predicted AM noise parameter satisfies the second threshold, generating the control data indicating that the control operation is to be triggered;
• and
• controlling the wind turbine in accordance with the control data.

A wind turbine system, comprising:

• a tower;
• a nacelle disposed on the tower;
• a rotor extending from the nacelle and having a plurality of blades at one end thereof;
• and
• an electronic device configured to control an operation, comprising:
• obtaining wind turbine data associated with the wind turbine system, wherein the wind turbine data is indicative of conditions of operation of the wind turbine system;
• obtaining a measured Amplitude Modulation (AM) noise parameter indicative of measured AM noise in a region at a distance from the wind turbine system;
• predicting an AM noise parameter indicative of AM noise within the region at the distance from the wind turbine system by applying a machine learning model to the wind turbine data;
• comparing the measured AM noise parameter and the predicted AM noise parameter;
• determining, based on the comparison between the measured AM noise parameter and the predicted AM noise parameter, a source of the AM noise near the wind turbine system;
• generating, based on the AM noise parameter, control data indicative of a control operation of the wind turbine system, wherein the control data comprises:
• determining whether the measured AM noise parameter exceeds a first threshold;
• determining whether the comparison between the measured AM noise parameter and the predicted AM noise parameter satisfies a second threshold;
• and
• only after determining that the measured AM noise parameter exceeds the first threshold and that the comparison between the measured AM noise parameter and the predicted AM noise parameter satisfies the second threshold, generating the control data indicating that the control operation is to be triggered;
• and
• controlling the wind turbine system in accordance with the control data.

A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed on any combination of one or more processors, carry out an operation controlling a wind turbine, the operation comprising:

• obtaining wind turbine data associated with the wind turbine, wherein the wind turbine data is indicative of conditions of operation of the wind turbine;
• obtaining a measured Amplitude Modulation (AM) noise parameter indicative of measured AM noise in a region at a distance from the wind turbine;
• predicting an AM noise parameter indicative of AM noise within the region at the distance from the wind turbine by applying a machine learning model to the wind turbine data;
• comparing the measured AM noise parameter and the predicted AM noise parameter;
• determining, based on the comparison between the measured AM noise parameter and the predicted AM noise parameter, a source of the AM noise near the wind turbine;
• generating, based on the AM noise parameter, control data indicative of a control operation of the wind turbine, wherein the control data comprises:
• determining whether the measured AM noise parameter exceeds a first threshold;
• determining whether the comparison between the measured AM noise parameter and the predicted AM noise parameter satisfies a second threshold;
• and
• only after determining that the measured AM noise parameter exceeds the first threshold and that the comparison between the measured AM noise parameter and the predicted AM noise parameter satisfies the second threshold, generating the control data indicating that the control operation is to be triggered;
• and
• controlling the wind turbine in accordance with the control data.