Research On Image Detection Of Wind Turbine Blade Defects

As the clean and efficient energy source,the wind energy plays an increasingly important role in the energy demand of modern society.With the working life of wind turbines increasing,the safe operation of wind turbine blades becomes particularly important.Wind turbine blades are the key components for capturing wind energy.From manufacture,transportation,installation to work,the surface and interior of wind turbine blade may be damaged in varying degrees.Wind farms are generally selected in areas with harsher weather conditions such as offshore and mountainous areas,which will increase the probability of blade damage,and it may even lead to serious safety production accidents.In order to find these defects early,scholars at home and abroad have done a lot of research work,but only from the inspection of the blade surface.It is impossible to detect the defects inside the blade.Based on this,this thesis mainly examines the surface and internal defects of wind turbine blades in visible light and infrared thermal wave images.The main contents are as follows:(1)Aiming at the adverse effects of foggy weather generated under severe weather conditions on the detection of small defects on the surface of wind turbine blades,the thesis presented a defogging algorithm based on the dark channel priori.The algorithm first calculated the atmospheric light value according to the characteristics of the dark channel of the fog image and its degradation model.Then it estimated the transmittance through mathematical derivation and needed to optimize.Finally,it brought the estimated atmospheric light value and transmittance into the foggy weather degradation model to solve for a clear image.The simulation results show that the algorithm can effectively remove the fog in the defect image of the blade,and can accurately detect the small defects on the surface of the blade.(2)Due to the special operating conditions of the blades,motion blur images will be produced when shooting it.In order to eliminate the motion blur in the image,first the thesis used the cepstrum method to estimate the blur parameters,and used error analysis to determine the feasibility of this method.Then,the thesis estimated the point spread function through the blur parameters,and substituted the point spread function into the restoration algorithm to process the motion blurred image.Finally,the thesis used the image restoration quality evaluation method to evaluate the best matching environment of the algorithm.The thesis used Pycharm development environment and Python development language to realize algorithm.The simulation results show that the cepstrum method can accurately estimate the blur parameters,and can restore the motion blur in the image well.(3)The thesis used infrared thermal wave images to realize the detection of internal defects of wind turbine blades.Firstly,the thesis used Auto CAD to model the nondestructive model and defect model of the blade,and imported the model into ANSYS software for thermal analysis.Then,the thesis exported the thermal analysis image to compare the thermal image of the defect model with the thermal image of the nondestructive model.Finally,the thesis performed ROI and edge extraction on the image and calculated edge data features to achieve defect classification.The simulation results show that the method can accurately extract the difference between the non-destructive and defect,and realize the classification of the defect image by the characteristics of the image data.