Provided is a warning light apparatus for at least one wind turbine including: at least one warning light, at least one sensor for detecting the presence of an object, a presence acquisition device connected to the at least one sensor the presence acquisition device being configured for: triggering the capturing of data through the at least one sensor, analysing the data captured through the at least one sensor for detecting the presence of objects, a warning controller connected to the at least one warning light and the presence acquisition device, the warning controller being configured for: receiving information about the detection of objects from the presence acquisition device, sending function signals depending on the information received from the presence acquisition device for activating or deactivating the at least one warning light.

Embodiments according to a first and second aspect of the present invention are based on the core idea of flying along the object for detecting a feature of an object and detecting at least a part of the object with a capturing unit with a first resolution and providing, for those areas of the object that comprise the feature, images with the second resolution that is higher than the first resolution.

Provided is a method for computer-implemented monitoring of wind turbines in a wind farm each wind turbine including, an upper section being pivotable around a vertical yaw axis wherein the following steps are performed: i) obtaining a digital image of the upper section of the first wind turbine, the image being a current image taken by a camera installed on the upper section of the second wind turbine; ii) determining a yaw misalignment angle between the first and second wind turbines by processing the image by a trained data driven model, where the image is fed as a digital input to the trained data driven model and the trained data driven model provides the yaw misalignment angle as a digital output, the yaw misalignment angle being the obtuse angle between the rotor axis of the first wind turbine and the rotor axis of the second wind turbine.

Provided is a method for computer-implemented monitoring of wind turbines in a wind farm each wind turbine including, an upper section being pivotable around a vertical yaw axis wherein the following steps are performed: i) obtaining a digital image of the upper section of the first wind turbine, the image being a current image taken by a camera installed on the upper section of the second wind turbine; ii) determining a yaw misalignment angle between the first and second wind turbines by processing the image by a trained data driven model, where the image is fed as a digital input to the trained data driven model and the trained data driven model provides the yaw misalignment angle as a digital output, the yaw misalignment angle being the obtuse angle between the rotor axis of the first wind turbine and the rotor axis of the second wind turbine.

Provided is a control system for operating a floating wind turbine under sea ice conditions. The control system includes a detection device configured for detecting a formation of ice in a critical zone around the floating wind turbine, and an ice inhibiting device configured for manipulating the floating wind turbine in such a manner that the critical zone is free of a threshold amount of the detected formation of ice. Furthermore, a floating wind turbine is provided which includes a wind rotor including a wind rotor including a blade, a tower, a floating foundation, and an above-described control system. Additionally, a method for operating a floating wind turbine under sea ice conditions is provided.

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.

A video monitoring method and system for a blade of a wind turbine are provided. The method includes: calculating a pan value and a tilt value based on each of a plurality of circular arc angles when the blade of the wind turbine is in a stationary state, wherein the plurality of circular arc angles are set based on a circular arc curve on a rotational plane of the blade, which is formed with a first point as a center and a first distance as a radius; and setting the pan value and the tilt value of a video camera to the calculated pan value and tilt value, respectively, to capture the blade.

A method and device for determining a tower clearance of a wind turbine. The method includes: acquiring an image of a wind turbine in operation (S10), the image comprising the tips of blades (2) and a tower (1) of the wind turbine; determining the positions of the tips of the blades (2) of the wind turbine in the image acquired (S20); identifying the edges of the tower (1) in the image acquired (30); and calculating, on the basis of the positions of the tips of the blades (2) and the edges of the tower (1) that have been determined, the distance from the tips (2) of the blades (2) to the edges of the tower (1) to acquire a tower clearance (S40).

Provided is a warning light apparatus for at least one wind turbine including: at least one warning light, at least one sensor for detecting the presence of an object, an presence acquisition device connected to the at least one sensor the presence acquisition device being configured for: triggering the capturing of data through the at least one sensor, analysing the data captured through the at least one sensor for detecting the presence of objects, a warning controller connected to the at least one warning light and the presence acquisition device, the warning controller being configured for: receiving information about the detection of objects from the presence acquisition device, sending function signals depending on the information received from the presence acquisition device for activating or deactivating the at least one warning light.

A method for assessing and inspection of wind turbine blades 4, in particular moving wind turbine blades, comprising the steps of directing a data capture device such as a camera system 1 towards a wind turbine blade 4 that is to be assessed. The camera system 1 can be attached to an aerial craft such as a helicopter 3, and is provided with a laser 13 that is used to track the motion of the blade 4 that is to be assessed. The laser 13 may be adapted to track a single blade 4 or the camera system 1 may be provided with multiple lasers to track multiple blades of the same turbine at the same time. The method further comprises collecting data of the state or condition of the blade 4 using the camera system 1 during the time that the helicopter 3 navigates around the wind turbine 2. The image data of the blade that is captured is fed into a computer processor (not shown) which can be on-board the helicopter 3 or at a remote location. The computer processor is adapted to reconstruct the image data into a 2-D or 3-D virtual digital image of the wind turbine 2. The method further comprises using at least one algorithm to compare and contrast various parts of the digital image generated by the reconstruction, with corresponding parts of a predetermined image of a healthy wind turbine, to identify defects or damage to the actual wind turbine, and the extent of the defects and damage. Using machine learning and A.I., the method is able to ascertain if and when replacement of the wind turbine blade may be necessary. An apparatus for undertaking the method is also claimed.