The disclosure relates to a wind turbine blade inspection system and method based on an unmanned aerial vehicle. The system includes: a collection module, used for collecting blade data and surrounding environment data of blades to be inspected, determining feature inspection points of the unmanned aerial vehicle according to the blade data and the surrounding environment data, and generating inspection paths; an inspection module, used for shooting corresponding blade at the feature inspection points according to the inspection paths to obtain a first inspection image and a second inspection image; an analysis module, used for receiving the first inspection image and the second inspection image, analyzing the first inspection image and the second inspection image to obtain a health state of the corresponding blade, and making a maintenance plan according to the health state of each of the blades.

A method for imaging a region of a moving blade of a wind turbine includes using a wider field-of-view (WFoV) camera to capture a plurality of WFoV images of at least part of the moving blade in a field-of-view (FoV) of the WFoV camera, determine a trigger time when an edge of the moving blade to calculate one or more NFoV image capture times when the edge of the moving blade, or a body of the moving blade, is, or will be, in the FoV of a NFoV camera, and using the NFoV camera to capture one or more NFoV images. The one or more NFoV images of the region of the moving blade may be analysed to identify any damage or defects in the moving blade without any need to interrupt the motion of the blades of the wind turbine.

An abnormality determination method for a wind power generation device includes: a measurement step (step S1) of measuring sound emitted by the wind power generation device and recording acoustic data; an analysis step (step S2) of performing a spectrogram analysis on the acoustic data recorded in the measurement step, on a frequency axis and in a temporal axis space as a temporal change in a frequency characteristic by using the short-time Fourier transform or the wavelet transform; a detection step (step S3) of detecting, from the analysis result in the analysis step, a signal component emitted from an abnormal portion of the wind power generation device in a time corresponding to rotation of the wind power generation device; and a determination step (step S5) of determining that the wind power generation device is abnormal when the signal component detected in the detection step is greater than or equal to a certain threshold value.

A state monitoring device for at least one rotor blade of a wind turbine, having at least one flexible sensor device with a plurality of measuring sections, which are arranged on a plurality of sections of the rotor blade and are designed so as to measure at least one respective parameter, and a processing device for detecting and/or processing the measured parameters. The invention device also relates to a rotor blade for a wind turbine, having at least one such state monitoring device, and to a wind turbine, having such a state monitoring device or such a rotor blade.

A wind turbine rotor blade imaging arrangement is provided, including a multi-axis gimbal mounted to the exterior of the wind turbine and configured to adjust its orientation in response to one or more received settings; a camera mounted on the multi-axis gimbal and arranged to capture images of a rotor blade; an image analysis unit configured to analyze the captured images; and a camera orientation controller configured to compute updated gimbal settings on the basis of the image analysis output.

Systems and methods for performing external inspections and internal inspections on wind turbine blades and correlating the external and internal inspections with one another are disclosed herein. A method of inspecting a wind turbine blade includes analyzing an exterior of the blade to produce external blade data, analyzing an interior of the blade to produce internal blade data, correlating a first position of the internal blade data with a second position of the external blade, and displaying an image including the first position and the second position.

The present disclosure is related to methods (400; 500; 600) configured for detecting the state of a wind turbine blade (22). The methods (400; 500; 600) comprising receiving (401; 501) load signals from a wind turbine blade (22), determining (402; 503) an energy of the load signal in a first and second frequency and comparing (403; 504) said energy to generate a flag signal if the energy in the first frequency is smaller than the energy in the second frequency. A control system (600) suitable to detect the state of a wind turbine blade (22) is also provided, as well as wind turbines (10) including such a control system (600).

It is described a method of performing deformation and/or orientation analysis of a wind turbine rotor blade, the method comprising: acquiring first position data of a first navigation system probe mounted at the blade to provide position at a first location; acquiring second position data of a second navigation system probe mounted at the blade to provide position at a second location; deriving first direction information at least regarding a relative direction of the first location and the second location based on the first position data and the second position data.

A system and method are provided for controlling a wind turbine. Accordingly, a component of the wind turbine is monitored by at least one sensor of a sensor system. An output is received from the sensor system which indicates a fault with the sensor. A fault accommodation response is generated by a fault module. The fault accommodation response includes an accommodation signal which replaces the output signal of the faulty sensor.

A robot includes front and rear traction modules to circulate throughout the inside of a wind turbine blade; two intermediate modules able to be inserted between the front module and the rear module, which include an intermediate machining module to machine fissures and cracks from within the blade; and an intermediate patching module to apply, compact and cure repair patches on the fissures and cracks; and a remote control system to monitor parameters and control the repair actions.