The present invention is a method of determining the average wind speed in a vertical plane by use of a LiDAR sensor (2), comprising performing measurements (MES), constructing a measurement model (MOD M) and a wind model (MOD V). Then an adaptive Kalman filter (KAL) is used to determine the wind speed (v), and determining the average wind speed in the vertical plane under consideration (RAWS).

Provided is a method of controlling at least one wind turbine in case of a rotational overspeed situation, the method including: determining a current state related to the wind turbine; providing data related to the current state as input to a turbine model; predicting a load of at least one wind turbine component and power output of the wind turbine using the turbine model provided with the input for plural control strategies; comparing the predicted load and power output for the plural control strategies; and selecting that control strategy among the plural control strategies that satisfies a target criterion including the load and the power output.

Disclosed is a method of establishing a wake management of a wind farm. The method comprises acts of monitoring one or more wake conditions using one or more sensors from one or more wind turbine generators (WTGs); and establishing a wake management of the wind farm as a function of the wake conditions. Disclosed is also a method of optimising operation of a wind turbine park based on wake management and a system for generating wake management.

A control system for positioning at least two floating wind turbines in a wind farm is provided. The control system includes a measuring device configured for measuring an incoming wind field at the two wind turbines, a determining device, wherein the determining device is configured for determining a wake property at the two wind turbines, wherein the determining device is configured for determining a propagation path of the wake property through the wind farm based on the determined wake property at the at least two floating wind turbines, wherein the determining device is configured for determining a location for each of the at least two floating wind turbines including a minimized wake influence based on the determined propagation path of the wake property through the wind farm, and a repositioning device configured for repositioning each of the at least two floating wind turbines to the determined location.

The object of the invention is a system recording the collisions of flying animals (9) with wind turbines (1) and indicating where they fell on the ground, which comprises a wind turbine (1) composed of a tower (2), a nacelle (3), a rotor (4) with blades (5) and a sensor unit comprising one sensor (6) and peripheral devices of the sensor, characterised in that the sensor (6) mounted on the nacelle (3) and/or tower (2) of the wind turbine (1) is a LIDAR sensor or a 3D light field camera or a 3D radar scanning the space around the wind turbine (1) in the field of view (7) of the sensor (6). The object of the invention is also the method of application of the above described system for recording the collisions of flying animals (9) with wind turbines (1) and indicating where they fell on the ground and the application of the system.

The present invention relates to a method of determining the direction of the wind by a LiDAR sensor (2). This method comprises performing measurements by the LiDAR sensor (2), deducing a Gaussian distribution of the longitudinal (u) and transverse (v) components of the wind speed, and determining wind direction (θ) by a spherical cubature approximation method and of the Gaussian distribution of the longitudinal and transverse components of the wind speed.

The present invention relates to a method of controlling a wind turbine by automatic online selection of a controller that minimizes the wind turbine fatigue. The method therefore relies on an (offline constructed) database (BDD) of simulations of a list (LIST) of controllers, and on an online machine learning step for determining the optimal controller in terms of wind turbine (EOL) fatigue. Thus, the method allows automatic selection of controllers online, based on a fatigue criterion, and switching between the controllers according to the measured evolution of wind condition.

A wind turbine blade is provided comprising a main blade portion and a light detection and ranging (LIDAR) element. The main blade portion has a shell defining an outer aerodynamic surface of the blade. The LIDAR element is disposed within a volume bounded by the outer aerodynamic surface and comprises at least one LIDAR system configured to transmit light beams away from the blade and to detect reflected light beams incident upon the blade.

A wind turbine and method are provided for defining a plurality of thrust limits for the wind turbine located at a site and having a rotor with rotor blades, wherein the thrust limits define values of aerodynamic thrust on the rotor not to be exceeded in operation. The method includes providing a wind speed distribution representative for the site and defining one or more isolines of constant turbulence probability representing a turbulence parameter as a function of wind speed. The isolines correspond to quantile levels of turbulence of the wind speed distribution and the turbulence parameter is indicative of wind speed variation. The turbulence parameter is determined by continuously measuring wind speed upstream of the rotor with an active sensing system and calculating the wind speed variations from the measured wind speed. Turbulence ranges are defined with respect to the isolines and thrust limits are defined for the turbulence ranges.

A wind turbine and method are provided for defining a plurality of thrust limits for the wind turbine located at a site and having a rotor with rotor blades, wherein the thrust limits define values of aerodynamic thrust on the rotor not to be exceeded in operation. The method includes providing a wind speed distribution representative for the site and defining one or more isolines of constant turbulence probability representing a turbulence parameter as a function of wind speed. The isolines correspond to quantile levels of turbulence of the wind speed distribution and the turbulence parameter is indicative of wind speed variation. The turbulence parameter is determined by continuously measuring wind speed upstream of the rotor with an active sensing system and calculating the wind speed variations from the measured wind speed. Turbulence ranges are defined with respect to the isolines and thrust limits are defined for the turbulence ranges.