Durability evaluation of PV modules using image processing tools

This paper presents the development of three image processing tools to analyze defects and predict performance of the photovoltaic modules using infrared thermography, electroluminescence and ultraviolet induced fluorescent images of the modules. The MATLAB processing tool uses an algorithm aimed at detecting defects and quantifying them in terms of area affected and intensity of the defect. Each image was studied for visual defects, processed and the results from the three techniques were compared. The algorithms lead to detection of defect location with high accuracy. The size and intensity of the defect was computed based on pixel information that was correlated with performance parameters like short circuit current, fill factor, and series resistance depending on the image processing technique used. The infrared image processing technique aided in hotspot detection and removing outliers with elevated cell temperatures for a correlative study with electroluminescence imaging. Electroluminescence image processing demonstrated linear correlation between the inactive cell area and performance parameters like fill factor and series resistance. Ultraviolet induced fluorescence image processing resulted in precise segmentation of browned area and showed a linear correlation with the short-circuit current drop. Ultraviolet induced fluorescence images indicated at the presence of cracks in cells with non-uniform browning based on the corresponding electroluminescence images. The modules in the study were from three different manufacturers to show that the processing tool can work for the different modules.