The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing
This review paper aims to evaluate the impact of defects on the reliability and degradation of photovoltaic (PV) modules during outdoor exposure. A comprehensive analysis of existing literature was conducted to identify the primary causes of degradation and failure modes in PV modules, with a particular focus on the effect of defects.
Although the terms ‘defects’ and ‘faults’ were interchangeably used in the literature, it was observed that the reference to ‘defects’ was typically related to the physical components or materials used in the PV system, such as physical anomalies in PV modules (e.g., cracks, hotspots, delamination, disconnections, etc.).
PV Panel Fault Detection PV panel fault detection is a technique that detects and diagnoses the failure of PV panels in solar PV systems. PV modules can suffer from common quality issues such as hot spots, cracks, and power degradation. These issues can impair the performance and lifespan of the components, and even pose safety risks [ 98 ].
This review paper provides valuable insights into the effect of defects on the performance of PV modules, and critical defects occur during outdoor exposure to PV modules which depend on the type of PV technology and outdoor environment conditions and are able to mitigate the further performance of PV modules.
In conclusion, this review highlights the significant impact of defects on the reliability and degradation of PV modules during outdoor exposure. The RPN analysis can effectively identify specific defects that have the greatest influence on module performance, including dust accumulation, module shading and humidity.
Main challenges of defect detection in PV systems. Although data availability improves the performance of defect diagnosis systems, big data or large training datasets can degrade computational efficiency, and therefore, the effectiveness of these systems. This limits the deployment of DL-based techniques in practical applications with big data.
