With the increasing adaption of solar energy worldwide, there is a huge interest to develop systems that help drive efficiency during manufacturing and ongoing operations. Due to various real-world conditions and processes, solar panels develop faults during their manufacturing and operations. The objective of this work is to build an End-to-End Fault Detection system to detect and localize faults in solar panels based on their Electroluminescence (EL) Imaging. Today, the majority of fault detection happens through manual inspection of EL images. To this end, we propose the design and implementation of an end-to-end system that firstly divides the solar panel into individual solar cells and then passes these cell images through a classification + detection pipeline for identifying the fault type and localizing the faults inside a cell. We propose a hybrid architecture that contains an ensemble of multiple CNN model architectures for classification and detection. The ensemble is capable of serving both – monocrystalline and polycrystalline solar panels. The proposed system significantly helps in increasing the efficiency of solar panels and reducing warranty and repair costs. We demonstrate the performance of the proposed system using an open EL image dataset with 95% of cell-level fault prediction accuracy and high recall. The proposed algorithms are applicable and can be extended for other solar applications that use RGB, EL, or thermal imaging techniques.<\/p>\n","protected":false},"excerpt":{"rendered":"
With the increasing adaption of solar energy worldwide, there is a huge interest to develop systems that help drive efficiency during manufacturing and ongoing operations. Due to various real-world conditions and processes, solar panels develop faults during their manufacturing and operations. 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