Photovoltaic Cell Defect Identification and Categorization Using Image Classification Model

Abstract

The rapid growth of solar energy adoption underscores the importance of maintaining the efficiency and reliability of photovoltaic (PV) cells. Defects in PV cells, whether caused by manufacturing inconsistencies or environmental factors, can significantly degrade performance and lead to power losses. This study proposes an automated defect identification and categorization system using state-of-the-art image classification models, particularly deep convolutional neural networks (CNNs). The system is trained on a labeled dataset of PV cell images encompassing both defective and non-defective categories, further classifying common defects such as cracks, discoloration, and hotspots. The proposed model achieved a classification accuracy of 97.44%, demonstrating robust performance in real-time defect detection. This AI-driven approach offers a scalable and non-invasive solution for quality assessment in solar panel manufacturing and maintenance, enhancing operational efficiency, reducing manual inspection costs, and supporting the sustainable deployment of solar energy systems.

Keywords: deep learning, Convolutional Neural Networks (CNN), Photovoltaic (PV) Cells, Defect Detection, Image Classification, Solar Panel Inspection, Automated Quality Assessment, Renewable Energy.