Abstract
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.
Highlights
In the past decades, the huge capacity of solar energy has been established around the world and the energy conversion efficiency of photovoltaic (PV) has achieved tremendous improvements year by year [1,2]
To address the aforementioned challenges, a novel approach using the combination of deep learning, deep clustering, and transfer learning is introduced to establish our defects detection system of PV cells with EL image, which can recognize the labeled and unknown or unlabeled defects with high performance
The technical contributions made in this work are as follows: 1) A deep clustering algorithm is designed to cluster the different unknown or unlabeled defects based on the distance difference of the defects feature vector that is extracted by the well-trained CNN-based model
Summary
The huge capacity of solar energy has been established around the world and the energy conversion efficiency of photovoltaic (PV) has achieved tremendous improvements year by year [1,2]. In [17], a deep learning-based defect detection of a photovoltaic module is proposed and GAN is introduced for the data augmentation. The solution used in the references could not be appropriate for the field inspection; (2) The dataset with sufficient images covering all defects can be hardly obtained, and the unknown or unlabeled defects may exist in the original dataset. This can degrade the defect detection performance; (3) Image annotation for the unknown or unlabeled defects can be time-consuming in practice. These limitations may significantly degrade the performance of automatic defect detection using EL images in terms of both efficiency and accuracy in large-scale photovoltaic plants
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