The invention relates to a method for identifying an asymmetrical extreme load which is caused by a gust of wind and acts on a wind power installation, wherein the wind power installation has a rotor having at least three rotor blades; the rotor blades are adjustable in terms of the blade angle thereof; and the rotor by way of the rotor blades thereof sweeps a rotor field; and the method comprises continuous detecting of a blade load for each rotor blade; ascertaining for at least one sector of the rotor field at least one temporal sector load profile from blade loads detected of different rotor blades with the same azimuth position, said sector load profile describing a temporal profile of a load on the rotor blades in the sector and containing a profile extrapolated for a future temporal period, wherein the blade loads are detected or taken into account at successive detection time points which are spaced apart by a partial period in which the rotor rotates further by one rotor blade, so that successive blade loads are detected or taken into account for the respective sector; and checking in terms of expecting an extreme load as a function of the at least one sector load profile.

Provided is a method of determining a control setting of at least one wind turbine of a wind park, the method including: determining a free-stream wind turbulence and deriving the control setting based on the free-stream wind turbulence, wherein the control setting includes a yawing offset, and wherein the yawing offset is derived to be the smaller, the higher the free-stream wind turbulence is.

A method of controlling pitch of individual blades in a wind turbine is described, together with a suitable controller. Wind speed is determined as a function of azimuthal angle. Wind speed is then predicted for individual blades over a prediction horizon using this determination of wind speed as a function of azimuthal angle. The predicted wind speed for each individual blade is used in a performance function, which is optimized to control individual blade pitches.

In one embodiment, a method includes determining, by a controller, a first wind pressure associated with a first port of a first probe, determining, by the controller, a second wind pressure associated with a second port of the first probe, and determining, by the controller, a reference wind pressure associated with an end portion of the first probe. The method also includes calculating, by the controller, a first reference differential using the first wind pressure and the reference wind pressure, calculating, by the controller, a first rotational differential using the first wind pressure and the second wind pressure, and calculating, by the controller, an angular coefficient using the first reference differential and the first rotational differential. The method further includes calculating, by the controller, a wind velocity using the first reference differential and the angular coefficient. The wind velocity represents a wind velocity relative to a vehicle.

A method for computer-implemented controlling of wind turbines in a wind farm is provided. The wind farm includes an upstream first and a downstream second wind turbines, wherein the following steps are performed: i) obtaining environmental data and stress data of the first wind turbine, the environmental data and the stress data being taken; ii) determining a status information indicating whether or not a predetermined event is present at the time of taking the data, wherein the predetermined event requires immediate controlling of the first wind turbine; iii) broadcasting a message which contains environmental data and a timestamp as event information; iv) evaluating the event information whether or not the predetermined event at the first wind turbine will hit the second wind turbine; v) generating a control command for controlling the second wind turbine in case the evaluation holds that the predetermined event will hit the second wind turbine.

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).

To solve the problem of a mis-calibration of a wind turbine a computer-implemented method for re-calibrating at least one yaw-angle of a wind turbine starting from an initial yaw-angle calibration of said wind turbine, based on determining a turbulence intensity estimation value (20) related to said appropriate yaw-angle (10), wherein the turbulence intensity (TI) being a ratio of wind speed deviation to average wind speed over a pre-determined period of time. Further, to solve the problem of a mis-calibration of a wind turbine a system for re-calibrating at least one yaw-angle of a wind turbine based on above re-calibration method. Further, to solve the problem of a management of a wind park below optimum a computer-implemented method for wind park optimization based on simulation calculation including turbulence intensity estimation values (20) estimating said at least one effecting wind turbine (101,102,103) to suffer from wake from said at least one effected wind turbine (100,101,102). Further, to solve the problem of a management of a wind park below optimum a wind park, including a management system for optimizing that wind park based on above optimization method. Moreover, present invention relates to a computer-readable medium comprising such methods.

A method for operating a wind turbine wherein a parameter for a wind hitting the wind turbine is determined from present values for the generator speed and/or the wind speed at each point in time (t). A temporal change variable is formed for the parameter at each point in time (t). For the temporal change variables, which occurred in a past time interval, the third and/or fourth statistical moment is calculated for a distribution of the temporal change values in the time interval. If at least one of the statistical moments exceeds a predetermined value, then a detection signal is set for an extreme gust, which triggers one or both of the steps: increasing a setpoint value for the blade pitch angle starting from an actual value thereof, and reducing a setpoint value for the generator speed starting from an actual value thereof.

A wind turbine has a Lidar device to sense wind conditions upstream of the wind turbine. Signals from the wind turbine are processed to detect an extreme change in wind direction. The detection is performed by differentiating the rate of change of wind direction and filtering for a period of time. On detection of extreme change the system controller takes the necessary evasive action which may include shutting down the turbine, commencing an immediate yawing action, and de-rating the turbine until the yawing action is complete.

A wind turbine has a Lidar device to sense wind conditions upstream of the wind turbine. Wind speed signals from the wind turbine are processed to detect an extreme operating gust. The detection is performed by differentiating the axial wind velocity and filtering for a period of time. On detection of extreme operating gust the system controller takes necessary evasive action which may include shutting down the turbine or varying the blade pitch angle.