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Automated inspection plays an important role in monitoring large-scalephotovoltaic power plants. Commonly, electroluminescense measurements are usedto identify various types of defects on solar modules but have not been used todetermine the power of a module. However, knowledge of the power at maximumpower point is important as well, since drops in the power of a single modulecan affect the performance of an entire string. By now, this is commonlydetermined by measurements that require to discontact or even dismount themodule, rendering a regular inspection of individual modules infeasible. Inthis work, we bridge the gap between electroluminescense measurements and thepower determination of a module. We compile a large dataset of 719electroluminescense measurementsof modules at various stages of degradation,especially cell cracks and fractures, and the corresponding power at maximumpower point. Here,we focus on inactive regions and cracks as the predominanttype of defect. We set up a baseline regression model to predict the power fromelectroluminescense measurements with a mean absolute error of 9.0+/-3.7$W_P$(4.0+/-8.4%). Then, we show that deep-learning can be used to train a modelthat performs significantly better (7.3+/-2.7$W_P$ or 3.2+/-6.5%) and propose avariant of class activation maps to obtain the per cell power loss, aspredicted by the model. With this work, we aim to open a new research topic.Therefore, we publicly release the dataset, the code and trained models toempower other researchers to compare against our results. Finally, we present athorough evaluation of certain boundary conditions like the dataset size and anautomated preprocessing pipeline for on-site measurements showing multiplemodules at once.

Deep Learning-based Pipeline for Module Power Prediction from EL Measurements