A method for controlling wind turbines. Incident signal data is obtained from wind turbines and fed to an artificial intelligence (AI) model in order to identify patterns in the incident signals generated by the wind turbines. One or more actions are associated to the identified patterns, based on identified actions performed by the wind turbines in response to the generated incident signals. During operation of the wind turbines, one or more incident signals from one or more wind turbines are detected and compared to patterns identified by the AI model. In the case that the detected incident signal(s) match(es) at least one of the identified patterns, the wind turbine(s) are controlled by performing the action(s) associated with the matching pattern(s).

The invention provides a method and a device for fault diagnosis of a yaw system in a wind turbine, and relates to the technical field of wind turbines. The method comprises the following steps: acquiring monitoring data collected in a yaw system, inputting the monitoring data into a yaw fault diagnosis model, and outputting a fault diagnosis result, wherein the fault diagnosis model is obtained by training known fault diagnosis results and corresponding monitoring data, and the fault diagnosis result comprises at least one of the following: the position of a yaw sensor is shifted, the yaw sensor is damaged, the yaw contactor is stuck, the hardware of a yaw motor/reducer is damaged, and the yaw motor is braked, In the working process, the monitoring data collected by the yaw system can be input into the yaw fault diagnosis model in real time, and the yaw fault diagnosis model can be used to determine whether the yaw system has a fault and the specific fault diagnosis results when the fault occurs. In this way, the operation and maintenance personnel can be prevented from going to the aircraft seat for inspection, and the fault diagnosis efficiency of the yaw system can be improved.