Algerian Journal of Renewable Energy and Sustainable Development
Volume 6, Numéro 1, Pages 44-52
2024-06-15
Authors : Rachid Zaghdoudi . Nadir Fargani .
This paper presents a novel approach for classifying infrared solar modules using a hybrid CNN-SVM model. The proposed method involves several key steps: preprocessing using histogram equalization to enhance image contrast, data augmentation to increase the diversity of the training set, feature extraction using a Convolutional Neural Network (CNN), and final classification with a Support Vector Machine (SVM) classifier. To evaluate the effectiveness of this approach, we used a comprehensive infrared solar modules dataset comprising 20,000 images. The hybrid model achieved an overall accuracy of 92.67%, with a precision of 90.85%, recall of 93.10%, and F1 score of 92.46%, demonstrating significant improvements over existing state-of-the-art methods. Comparative analysis with recent studies further validates the effectiveness of our approach. This work underscores the potential of combining deep learning with traditional machine learning techniques for enhanced solar module inspection and quality assurance.
Solar energy Defect detection CNN SVM Thermography PV modules
Bounadja Mohamed
.
Belarbi Ahmed Wahid
.
Belmadani Bachir
.
pages 41-47.
Elfoul Lantri
.
pages 19-38.
Labbi A.
.
Mokhnache A.
.
pages 109-121.
Dib Abderrahmane
.
Djermane Ali
.
pages 28-32.
Razagui A.
.
Bouchouicha K.
.
Bachari N.e.i.
.
pages 601-612.