A method for identifying a blade run-away condition in the event of a pitch system failure of a rotor blade of a wind turbine includes determining, via one or more sensors, an actual rotor loading of the wind turbine. The method also includes determining, via a turbine controller, an estimated rotor loading of the wind turbine based on at least one of one or more operating conditions of the wind turbine or one or more wind conditions of the wind turbine. Further, the method includes determining a difference between the actual rotor loading and the estimated rotor loading. The method also includes determining whether the blade run-away condition is present based on the difference. The method may also include implementing a corrective action that mitigates loads caused by the blade run-away condition.

The present disclosure relates to a system for determining at least one blade state parameter of a wind turbine blade, wherein the system is configured to: obtain blade data relating to the wind turbine blade from a sensor system associated with the wind turbine blade; compare at least one reference model of at least a portion of the wind turbine blade with the blade data; identify a reference model in dependence on the comparison; and determine at least one blade state parameter in dependence on the identified reference model. The blade data may take the form of an image, for example a 3-dimensional measurement such as a point cloud representing at least a portion of the blade.

The present invention is a method for predicting the wind speed in the rotor plane (PR) of a wind turbine (1), by accounting for an induction factor used in a wind evolution model implemented by a Kalman filter. The invention also is a method for controlling a wind turbine (1), a computer program product, a LiDAR sensor (2) and a wind turbine (1), which uses the wind prediction determined with the method according to the invention.

Described is a system for monitoring deflection of turbine blades of a wind turbine comprising a tower. The system comprises a position detecting apparatus mounted to the wind turbine comprising a plurality of position detection components each collecting data regarding a field of detection through which a segment of the turbine blades passes, wherein the position detection components are monitoring distinct fields of detection to collect distances of a plurality of segments of each one of the turbine blades travelling through the fields of detection. The system further comprises a deflection controller configured to receive the collected distances and to determine deflection of the turbine blades accordingly. An associated method comprises collecting distances of a plurality of distinct segments of the turbine blades when the turbine blades travel within a plurality of fields of detections, and processing the collected distances to determine clearance between the turbine blades and the tower.

A wind turbine includes a wind turbine rotor and rotor blades mounted on the rotor, at least one sensing device disposed on the wind turbine for measuring a first signal representative of a first wind speed at a first distance from the wind turbine rotor and a second signal representative of a second wind speed at a second distance from the wind turbine rotor. The wind turbine system includes a blade pitch actuator for adjusting a pitch of the rotor blades and a generator controller for adjusting a voltage of a wind turbine generator. The wind turbine system also includes a control unit in communication with the blade pitch actuator and the generator controller, the control unit being used for controlling the wind turbine via the blade pitch actuator and the generator controller based on an induction factor derived from the first and second signals.

Systems, devices, and methods for improving LIDAR-based turbulence intensity (TI) estimates are described. An example system may include a LIDAR instrument configured to determine, based on reflections of emitted light, a plurality of wind speed values. The system also includes a physics-based error correction module configured to determine, based on the wind speed values, at least one LIDAR-based meteorological characteristic value, and determine, based on the LIDAR-based meteorological characteristic value and at least one physical characteristic of the LIDAR instrument, at least one modified meteorological characteristic value. The system further includes a statistical error correction module configured to determine, based on the modified meteorological characteristic value and a meteorological characteristic error model generated using collocated LIDAR-based meteorological characteristic values and in situ instrument-based meteorological characteristic values, at least one corrected TI estimate, and output the corrected TI estimate.

A method for predicting arrival of a wind event at an aeromechanical structure includes sensing wind velocity in an atmospheric volume moving towards the aeromechanical structure to obtain a time series of spatially distributed wind velocity measurements, determining presence of the wind event from at least one of the distributed wind velocity measurements, and tracking the wind event based upon the time series of spatially distributed wind velocity measurements to estimate arrival time of the wind event at the aeromechanical structure. A wind-predicting system uses a lidar and a wind-predicting unit to implement this method. An aeromechanical apparatus integrates this wind-predicting system to predict wind events at the aeromechanical apparatus.

The invention relates to a method for detecting aberrant values of an incident wind field in a space located upstream of a lidar sensor. The method comprises acquiring and modelling a measurement rws(k) with the lidar sensor of an incident wind field, by estimating a median mr(k) and a mean absolute deviation dr(k) in real time of measurements of the incident wind field and detecting aberrant values in real time using the estimated median mr(k) and the mean absolute deviation dr(k).

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 is a method of determining an induction factor of the wind for a wind turbine (1) equipped with a LiDAR sensor (2). For this method, wind speed measurements are performed in measurement planes (PM) by use of LiDAR sensor (2), then induction factors between measurement planes (PM) are determined by use of the measurements and of a linear Kalman filter, and the induction factor between a measurement plane (PM) and the rotor plane (PR) of wind turbine (1) is determined by a second linear Kalman filter.