QUYOSH FOTOELEKTRIK PANELLARIDAGI NOSOZLIKLARNI AXBOROT TEXNOLOGIYALARI ASOSIDA TASNIFLASH VA DIAGNOSTIKA QILISH

Abstract

This article examines the main faults that occur in photovoltaic solar panels during operation and the issues of their detection and classification using modern information technologies. Micro- and macro-cracks, hotspots, partial shading, soiling, delamination, corrosion, Potential-Induced Degradation (PID), as well as failures of bypass diodes and electrical contacts in solar panels are analyzed. The causes of faults, their visual characteristics, effects on electrical and thermal parameters, and available detection methods are systematized. The study analyzes the potential of Computer Vision, digital image processing, Machine Learning, and Deep Learning technologies for solar panel diagnostics. The possibility of automatically detecting and classifying faults through the processing of RGB, thermal, and electroluminescence images using artificial intelligence algorithms is substantiated. The results of the study provide a theoretical basis for the development of intelligent condition-monitoring and automated diagnostic systems for photovoltaic installations.