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首页> 外文期刊>Renewable Power Generation, IET >Photovoltaic cell defect classification using convolutional neural network and support vector machine
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Photovoltaic cell defect classification using convolutional neural network and support vector machine

机译：使用卷积神经网络和支持向量机的光伏电池缺陷分类

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摘要

Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state-of-the-art methods. With features extraction-based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.
机译：由于手动/目视检查的应用有限，光伏（PV）模块中的自动缺陷分类越来越大。然而，由于细胞裂缝和复杂背景的非均匀强度，晶体硅太阳能电池中的缺陷的自动分类是一个具有挑战性的任务。对电致发光图像中的PV电池进行自动缺陷分类，进行本研究。两种机器学习方法，特征采用提取的支持向量机（SVM）和卷积神经网络（CNN）用于太阳能电池缺陷分类。合适的超参数，算法优化器和丢失功能用于实现最佳性能。太阳能电池缺陷分为七种类，例如一个无缺陷和六种缺陷的课程。特征提取算法，例如取向梯度（Hog），Kaze，尺度不变特征变换（SIFT）和加速鲁棒特征（SURD）的直方图用于训练SVM分类器。最后，比较了性能结果。得出结论是，CNN的太阳能电池缺陷分类的准确性为91.58％，这优于最先进的方法。具有基于提取的提取的SVM，分别获得69.95,71.04,68.9​​0和72.74％的精度，分别用于猪，Kaze，Sift和Surf。本研究可能有助于使PV系统更有效地对缺陷进行分类以提高电力系统效率。

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