Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants

Defects detection with Electroluminescence(EL)image for photovoltaic(PV)module has become a standard test procedure during the process of production,installation,and operation of solar modules.There are some typical defects types,such as crack,finger interruption,that can be recognized with high accuracy.However,due to the complexity of EL images and the limitation of the dataset,it is hard to label all types of defects during the inspection process.The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique.To address the problem,we proposed an evolutionary algorithm combined with traditional image processing technology,deep learning,transfer learning,and deep clustering,which can recognize the unknown or unlabeled in the original dataset defects automatically along with the increasing of the dataset size.Specifically,we first propose a deep learning-based features extractor and defects classifier.Then,the unlabeled defects can be classified by the deep clustering algorithm and stored separately to update the original database without human intervention.When the number of unknown images reaches the preset values,transfer learning is introduced to train the classifier with the updated database.The fine-tuned model can detect new defects with high accuracy.Finally,numerical results confirm that the proposed solution can carry out efficient and accurate defect detection automatically using electroluminescence images.

Far-infrared electroluminescence characteristics of an Si-based photodiode under a forward DC bias current

At room temperature, the bias dependence of a far-infrared electroluminescence image of a photodiode is investi-gated in the dark condition. The results show that the electroluminescence image can be used to detect defects in the photodiode. Additionally, it is found that the electroluminescence intensity has a power law dependence on the dc bias current. The photodiode ideality factor could be obtained by a fitting a relationship between the electroluminescence intensity and the bias current. The device defect levels will be easily determined according to the infrared image and the extracted ideality factor value. This work is of guiding significance for current solar cell testing and research.