Wind turbine blade defect detection using hyperspectral imaging

Regardless of the evolution in the wind turbine industry, the operation of wind farms faces critical challenges when it comes to maintaining the lowest possible cost of energy. It is essential to early detect or predict wind turbine breakdowns due to different factors such as material degradation, electrical or mechanical failures, faults, or environmental damage. Wind turbine blades are the most expensive and most exposed parts of a wind turbine and suffer from many shortcomings, mainly cracks and erosion, which reduces their performance. Hence, there is an essential requirement for using non-destructive diagnostic of wind turbine blades. This paper lists some of the current non-destructive techniques for wind turbine blades analysis, their applicability, advantages, and drawbacks. Nevertheless, these methods face drawbacks that can be overcome by remote sensing. Hyperspectral imaging is a spectral imaging technique that integrates imaging and spectroscopy. It also enables the analysis and identification of distinctive spectral signatures and assigns them to the examined sample elements. Thus, this paper describes hyperspectral imaging implementation in image acquisition, handling, and flaw recognition as well as the detection of cracks and erosion. This technique's field output results show that blade defect detection's accuracy and precision are significantly enhanced.