Using CNNs for Photovoltaic Panel Defect Detection via Infrared Thermography to Support Industry 4.0

Abstract

This study demonstrates how convolutional neural networks (CNNs), supported by open-source software and guided by corporate social responsibility (CSR), can enhance photovoltaic (PV) panel maintenance. Connecting industrial informatics with sustainable practices underscores the potential for more efficient and responsible energy systems within Industry 4.0. The rapid expansion of solar power necessitates effective maintenance and inspection of PV panels to ensure optimal performance and longevity. CNNs have emerged as potent tools for detecting defects in PV panels through infrared thermography (IRT).

Keywords

• convolutional neural networks
• photovoltaic panels
• defect detection
• infrared thermography
• solar energy
• q42
• q55
• q56