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All Journal Jurnal Ilmiah Teknologi dan Rekayasa International Journal of Advances in Intelligent Informatics
Cahyaningtyas, Rizqia
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Solar module defects classification using deep convolutional neural network Cahyaningtyas, Rizqia; Madenda, Sarifuddin; Bertalya, Bertalya; Indarti, Dina
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1818

Abstract

Solar modules are essential components of a solar power plant, that are designed to withstand scorching heat, storms, strong winds, and other natural influences. However, continuous usage can cause defects in solar modules, preventing them from producing electrical energy optimally. This paper proposes the development of a deep learning-based system for identifying and classifying solar module surface defects in solar power plants. Module surface condition are classified into five categories: clean, dirt, burn, crack, and snail track. The dataset used consists of 8,370 images, including primary image data acquired directly from the mini solar power plant at the Renewable Energy Laboratory of PLN Institute of Technology, and secondary image data obtained from public repositories. The limitation in the number of images in each category was overcome using data augmentation techniques. The proposed classification model combines Deep Convolutional Neural Networks (DCNN) with transfer learning models (DenseNet201, MobileNetV2, and EfficientNetB0) to perform supervised image classification. Training and testing results on the three models demonstrated that the combination of DCNN + DenseNet201 provided the best performance, with a classification accuracy of 97.85%, compared to 97.25% accuracy for DCNN + EfficientNetB0 and 94.98% for DCNN + MobileNetV2. This research shows that DCNN-based image classification reliably diagnoses solar module defects and supports using RGB images for surface defect classification. Applying the developed system to solar power plant maintenance management can help in accelerating the process of identifying panel defects, determining defect types, and performing panel maintenance or repairs, while ensuring optimal power production.
Deteksi Kerusakan Modul Surya Menggunakan Faster R-CNN ResNet-50 Ikhsan, Fathirul; Cahyaningtyas, Rizqia; Kuswardani, Dwina
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i3.116

Abstract

Solar modules play a crucial role in photovoltaic power generation systems, yet their performance can degrade due to physical and electrical damage. Therefore, automatic inspection is required to improve maintenance efficiency and prevent long-term performance loss. This study aims to implement an object detection approach for identifying solar module defects from visible RGB images using Faster R-CNN with a ResNet-50 backbone. The dataset was obtained from the Kaggle platform and manually annotated into PASCAL VOC format with two defect classes, namely physical damage and electrical damage, and expanded through data augmentation. The model was trained under several training configurations and evaluated using mean Average Precision (mAP), precision, recall, F1-score, and accuracy. The best performance was achieved using a batch size of 8, learning rate of 0.0001, and 30 epochs, resulting in 89% accuracy and 93% mAP. The results indicate that the model consistently detects both defect types and demonstrates potential for automated solar module inspection.
Co-Authors Bertalya Bertalya Dina Indarti Dwina Kuswardani, Dwina Ikhsan, Fathirul Sarifuddin Madenda