Comparative investigation of imaging techniques, pre-processing and visual fault diagnosis using artificial intelligence models for solar photovoltaic system – A comprehensive review

Abstract

Photovoltaic systems provide an eco-friendly key to meet our increasing energy demand while mitigating the adverse impacts of conventional fossil fuel-based energy generation. The effective power generation from PV systems can be obtained from fault-free systems. Detecting and correcting faults in solar photovoltaic (PV) systems is vital to ensure their best performance, safety and durability. In the existing literature, some have concentrated only on possible faults in PV, some on inspection techniques for monitoring, and some on Machine learning models applied in fault detection. In this work, a combined review of the types of possible PV system failures, image acquisition methodologies, preprocessing techniques, and artificial intelligence (AI) models could accurately localise and distinguish the faults in PV systems that are presented. This work discusses different literature on automatic fault detection methodologies. This enables readers to focus on critical aspects while developing a practical fault detection technique for Solar PV systems.