A YOLOv12-based Framework for Failure Diagnosis in Photovoltaic System Modules

Abstract

Solar photovoltaic (PV) systems are crucial for reducing dependence on fossil fuels and carbon emissions, but they face challenges in terms of failures that can impact their efficiency. To effectively monitor and diagnose these failures, advanced tools are needed. Current monitoring systems collect data efficiently but struggle to analyze and diagnose it accurately. This leads to delayed fault detection and increased maintenance costs. The complexity of faults further complicates the diagnostic process. In this context, deep learning techniques offer a promising solution. The YOLOv12 (You Only Look Once, Version 12) framework, a state-of-the-art deep learning model based on Convolutional Neural Networks (CNN), is proposed as an advanced tool for PV system fault diagnosis. Compared to previous models, YOLOv12 demonstrates superior accuracy and speed, enabling more effective fault detection and classification. This tool improves the identification of fault types and evaluation of PV panel condition, ensuring timely maintenance and maintaining system reliability. AI approaches, particularly deep learning techniques, show great potential in enhancing monitoring and diagnostics, ultimately improving production efficiency and ensuring the long-term sustainability of photovoltaic systems.