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.

An apparatus for inspecting an adhesive layer applied about the leading edge of a wind turbine rotor blade is provided, which apparatus includes a heating assembly configured to direct heat at a portion of the adhesive layer between the outer edges of the adhesive layer; an infrared imaging means arranged to obtain an infrared image of a heated portion; and a displacement means adapted to move the inspection apparatus alongside the rotor blade during operation of the heating assembly and the infrared imaging means to facilitate infrared imaging of the adhesive layer.

The application provides a method, apparatus and electronic device for monitoring tower clearance. The method for monitoring tower clearance includes: obtaining a distance to an obstacle measured by a laser ranging device; calculating a measured clearance value of each laser based on a first distance of the laser; compensating the measured clearance value of each laser by a preset compensation formula to obtain a compensated clearance value of the laser; comparing a minimum compensated clearance value among N lasers with a preset clearance threshold; and performing safety protection for the wind turbine based on a comparison result.

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.

A detection unit acquires detection data of a wind turbine generator. A controller generates maintenance information of the wind turbine generator based on the detection data. The controller transmits maintenance information to a maintenance terminal.

The present disclosure provides a defect-detectable fiber fabric, including a plurality of bunches of fibers and a plurality of braided threads configured to respectively fix together fibers of each bunch of fibers. The plurality of braided threads include first braided threads each having a color different from a color of any of the plurality of bunches of fibers, and a respective distance between every two adjacent first braided threads is configured to make wrinkle defects that could be formed in fabric layers detectable. After the curing and molding, the positions of the defects and the layer(s) where the defects are formed can be determined according to the states of the first braided threads.

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.

A system for monitoring properties within a subsea electrical architecture of an offshore windfarm including one or more wind turbines incudes first passive optical sensors within the subsea unit for monitoring an electrical or environmental property within the subsea unit, a first optical fibre bundle extending integrally within a power cable, a first optical interconnection unit within the subsea unit and optically coupling one or more optical fibres of the optical fibre bundle to the passive optical sensors, a monitoring unit located at an onshore grid connection point, and a second optical interconnection unit optically coupling one or more optical fibres of the optical fibre bundle to said monitoring unit. The monitoring unit is configured to transmit monitoring light signals along one or more optical fibres of the first optical fibre bundle to said first optical interconnection unit and to localise a fault and/or operate a circuit breaker in dependence upon optical signals transmitted from the or each first passive optical sensor over the first optical fibre bundle.

The present disclosure provides a system and method for real-time anomaly data determination for turbine blades of a wind turbine. The system receives sensor data associated with a set of turbine blades of the wind turbine. The sensor data indicates one or more structural characteristics associated with each of the set of turbine blades and/or one or more operational characteristics associated with each of the set of turbine blades. The system determines profile data associated with each of the set of turbine blades based on the sensor data. The system determines anomaly data associated with at least one of the set of turbine blades based on the profile data associated with each of the set of turbine blades. The anomaly data indicates a deviation between at least two turbine blades of the set of turbine blades. The system outputs the anomaly data.