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Photovoltaic (PV) power systems have a significant potential to reduce greenhouse gases and diversify the electricity generation mix. Faults and damages that cause energy losses are common during either the fabrication or lifetime of PV modules. The development of automatic and reliable techniques to identify and classify faults in PV modules can help to improve the reliability and performance of PV systems and reduce operation and maintenance costs. A combination of infrared thermography and machine learning methods has been proven effective in the automatic detection of faults in large-scale PV plants. However, so far, few studies have assessed the challenges and efficiency of these methods applied to the classification of different defect classes in PV modules. In this study, we investigate the effect of data augmentation techniques to increase the performance of our proposed convolutional neural network (CNNs) to classify anomalies, between up to eleven different classes, in PV modules through thermographic images in an unbalanced dataset. Confusion matrices are used to investigate the high within- and between-class variation in different classes, which can be a challenge when creating an automatic tool to classify a large range of defects in PV plants. Through a cross-validation method, the CNN's testing accuracy was estimated as 92.5% for the detection of anomalies in PV modules and 78.85% to classify defects for eight selected classes. |
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