Determining an action to allow resumption wind turbine operation after a stoppage

1 . A computer-implemented method for a wind turbine, the method comprising:

receiving alarm state data indicating that the wind turbine has entered an alarm state in which operation of the wind turbine has stopped;

receiving sensor data from a plurality of sensors of the wind turbine indicative of operating conditions associated with the wind turbine;

executing a trained machine learning model based on the received sensor data and the alarm state data to obtain an output, wherein the trained machine learning model is trained on historical data associated with a plurality of wind turbines, and wherein the historical data includes data related to the plurality of wind turbines previously being in the alarm state;

based on the obtained output, performing an action to allow the wind turbine to resume operation, wherein the action comprises adjusting a pitch angle of rotor blades for the wind turbine or adjusting a speed of a generator in a nacelle of the wind turbine;

monitoring one or more parameters indicative of wind turbine operation after the action has been performed to determine a result indicating whether the alarm state has been resolved, wherein the result indicates an error state will reoccur within a defined period after the action has been performed, and wherein the result indicates a time until the error state reoccurs;

in response to determining the alarm state has not been resolved, providing a further action to be performed to allow the wind turbine to resume operation, wherein the further action is determined based on the performed action that did not resolve the alarm state; and

in response to performing the further action, resuming operation of the wind turbine.

2 . The method of claim 1 , the historical data being a training data set linking sensor data indicative of operating conditions when the wind turbine has previously been in the alarm state to actions performed in response to the wind turbine being in the alarm state.

3 . The method of claim 2 , wherein the training data set is labelled with a result of the action performed in response to each previous occurrence of the alarm state, and wherein the result indicates whether the performed action resolved the alarm state.

4 . The method of claim 2 , further comprising:

generating a new training data set with data from the plurality of sensors from an overall set of sensors of the wind turbine based on a degree to which the sensor data influenced the performed action in previous alarm states; and

training the trained machine learning model based on the new training data set.

5 . The method of claim 1 , wherein the result further indicates a different error state occurs within the defined period after the action has been performed.

6 . The method of claim 1 , further comprising:

updating training data set by adding the received sensor data, the performed action, and the determined result thereto;

retraining the trained machine learning model based on the updated training data set; and

wherein the historical data is the training data set linking sensor data indicative of operating conditions when the wind turbine has previously been in the alarm state to actions performed in response to the wind turbine being in the alarm state.

7 . The method of claim 1 , wherein the further action comprises:

automatically controlling the wind turbine to resume operation;

automatically adjusting a setting of one or more components of the wind turbine;

automatically adjusting a mode of operation of the wind turbine;

automatically dispatching service personnel to a site of the wind turbine;

providing a notification that human input is needed to allow the wind turbine to resume operation, wherein the human input includes dispatching service personnel to the site of the wind turbine; or

adjusting one or more parameter threshold values defining when the wind turbine enters the alarm state.

8 . The method of claim 1 , wherein the output is:

a selection of one action from a plurality of possible actions that may be performed; or

a likelihood of success associated with each of the respective possible actions, the likelihood of success indicating a likelihood that the respective action will resolve the alarm state.

9 . The method of claim 1 , wherein the alarm state is caused by the wind turbine entering the error state, wherein the trained machine learning model is executed further based on received event data, and wherein the received event data includes data relating to one or more events in which the wind turbine previously entered the error state but operation of the wind turbine was not stopped.

10 . The method of claim 9 , wherein the trained machine learning model is trained based on the received event data.

11 . The method of claim 1 , wherein the trained machine learning model is a neural network model, and wherein training the trained machine learning model includes adjusting one or more weights or other parameters of an architecture of the neural network model based on the historical data.

12 . A controller for a wind turbine, comprising:

an I/O interface configured for communicative coupling with the wind turbine; and

one or more processors configured to:

receive alarm state data indicating that the wind turbine has entered an alarm state in which operation of the wind turbine has stopped;

receive sensor data from a plurality of sensors of the wind turbine indicative of operating conditions associated with the wind turbine;

execute a trained machine learning model based on the received sensor data and the alarm state data to obtain an output, wherein the trained machine learning model is trained on historical data associated with a plurality of wind turbines, and wherein the historical data includes data related to the plurality of wind turbines previously being in the alarm state;

based on the obtained output, performing an action to allow the wind turbine to resume operation, wherein the action comprises adjusting a pitch angle of rotor blades for the wind turbine or adjusting a speed of a generator in a nacelle of the wind turbine;

monitoring one or more parameters indicative of wind turbine operation after the action has been performed to determine a result indicating whether the alarm state has been resolved, wherein the result indicates an error state will reoccur within a defined period after the action has been performed, and wherein the result indicates a time until the error state reoccurs;

in response to determining the alarm state has not been resolved, providing a further action to be performed to allow the wind turbine to resume operation, wherein the further action is determined based on the performed action that did not resolve the alarm state; and

in response to performing the further action, resume operation of the wind turbine.

13 . The controller of claim 12 , wherein the historical data is a training data set linking sensor data indicative of operating conditions when the wind turbine has previously been in the alarm state to actions performed in response to the wind turbine being in the alarm state.

14 . The controller of claim 13 , wherein the training data set is labelled with a result of the action performed in response to each previous occurrence of the alarm state, and wherein the result indicates whether the performed action resolved the alarm state.

15 . A wind turbine, comprising:

a tower;

a nacelle disposed on the tower;

a rotor extending from the tower and having a plurality of blades disposed on a distal end thereof; and

a controller configured to perform an operation, comprising:

receive alarm state data indicating that the wind turbine has entered an alarm state in which operation of the wind turbine has stopped;

receive sensor data from a plurality of sensors of the wind turbine indicative of operating conditions associated with the wind turbine;

execute a trained machine learning model based on the received sensor data and the alarm state data to obtain an output, wherein the trained machine learning model is trained on historical data associated with a plurality of wind turbines, and wherein the historical data includes data related to the plurality of wind turbines previously being in the alarm state;

based on the obtained output, performing an action to allow the wind turbine to resume operation, wherein the action comprises adjusting a pitch angle of the plurality of blades for the wind turbine or adjusting a speed of a generator in the nacelle;

monitoring one or more parameters indicative of wind turbine operation after the action has been performed to determine a result indicating whether the alarm state has been resolved, wherein the result indicates an error state will reoccur within a defined period after the action has been performed, and wherein the result indicates a time until the error state reoccurs;

in response to determining the alarm state has not been resolved, providing a further action to be performed to allow the wind turbine to resume operation, wherein the further action is determined based on the performed action that did not resolve the alarm state; and

in response to performing the further action, resume operation of the wind turbine.

16 . The wind turbine of claim 15 , wherein the historical data is a training data set linking sensor data indicative of operating conditions when the wind turbine has previously been in the alarm state to actions performed in response to the wind turbine being in the alarm state.

17 . The wind turbine of claim 16 , wherein the training data set is labelled with a result of the action performed in response to each previous occurrence of the alarm state, and wherein the result indicates whether the performed action resolved the alarm state.