Özet:

Detection and classification of faults in photovoltaic (PV) module cells have become a very important issue for the efficient and reliable operation of solar power plants. In this study, an efficient convolutional neural network (CNN) model is proposed for fast and accurate detection and classification of faults in PV module cells. The proposed model is developed with SqueezeNet, which has fewer parameters and model size, using the transfer learning approach. In order to improve the training convergence and increase the classification performance, the activation functions of the proposed model are changed and skip connections are added from the fire modules. A dataset obtained from Electroluminescence (EL) images are used in the experiments. Data augmentation techniques are also applied to eliminate the imbalance of class distribution and increase the class samples. The performance of the proposed method is compared with pre-trained CNN architectures such as AlexNet, ShuffleNet, GoogLeNet, and SqueezeNet. In the experimental studies, the accuracy, precision, sensitivity, specificity, and F1-score values of the proposed method are obtained as 91.29%, 84.21%, 89.72%, 92.04%, and 86.88%, respectively. In addition, the proposed method improves the accuracy values of the other methods between 0.99% and 6.29%. When all the obtained results are analyzed, it is observed that the proposed method has a superior performance in the detection of faults in PV module cells.

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