Abstract
A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.
Highlights
With the significant increase in the necessity of photovoltaic (PV) energy generation to curb climate change, the installation of large PV plants has grown significantly in the last decade [1]
This study investigates the use of machine learning (ML) to classify the defects mentioned above
It can be concluded that the larger number of pixel intensity features in the case of V2 (256) masks the unique features (16) that are used by V1, substantially reducing the performance of the ML classifiers
Summary
With the significant increase in the necessity of photovoltaic (PV) energy generation to curb climate change, the installation of large PV plants has grown significantly in the last decade [1]. As it is desirable to operate these plants at their maximum capacity, monitoring the performance of the installed PV modules is critical [2]. Cracks in solar cells have received significant attention in the last years [3]. A micro-crack Mode A does not have a significant impact on the output power. The loss due to the impacted cell area is relatively low, as long as the different regions are electrically connected [4]
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