Thermal Imaging and AI in Solar Panel Defect Identification

Abstract

Thermal imaging and artificial intelligence (AI) have emerged as promising technologies for defect identification in solar panels, offering non-destructive, efficient, and accurate inspection methods. This paper presents a comprehensive review of the applications of thermal imaging and AI techniques in the detection and classification of defects in solar panels, with a focus on their advantages, challenges, and future prospects. The integration of thermal imaging with AI algorithms enables automated detection and analysis of various types of defects, including cracks, delamination, hotspots, and corrosion, without the need for manual intervention. Thermal imaging captures infrared radiation emitted by solar panels, allowing for the visualization of temperature variations associated with defects. AI algorithms, such as convolutional neural networks (CNNs) and support vector machines (SVMs), process thermal images to identify and classify defects based on their unique thermal signatures. Key advantages of thermal imaging and AI for solar panel defect identification include rapid inspection speed, high accuracy, and compatibility with large-scale solar installations. These technologies offer real-time monitoring capabilities, enabling early detection of defects and proactive maintenance actions to prevent performance degradation and costly repairs. Additionally, thermal imaging provides valuable insights into panel health and performance, facilitating data-driven decision-making for solar asset management.