Photovoltaic panel flaw detection

Why is fault diagnosis important for photovoltaic systems?

The reliable performance and efficient fault diagnosis of photovoltaic (PV) systems are essential for optimizing energy generation, reducing downtime, and ensuring the longevity of PV installations.

What is PV panel defect detection?

The task of PV panel defect detection is to identify the category and location of defects in EL images.

Can a neuro-fuzzy system detect faults in photovoltaic systems?

In Zyout and Oatawneh, 2020, Mansouri et al., 2021 and Chen et al. (2020), an adaptive neuro-fuzzy system for the fault diagnosis and removal of faults in photovoltaic (PV) systems is proposed. The proposed model conducts an ageing study on various panels and obtains a variety of behaviors in identifying problems.

Can a real-time defect detection model detect photovoltaic panels?

Efforts have been made to develop models capable of real-time defect detection, with some achieving impressive accuracy and processing speeds. However, existing approaches often struggle with feature redundancy and inefficient representations of defects in photovoltaic panels.

How to detect a defect in a photovoltaic module using electroluminescence images?

An intelligent algorithm for automatic defect detection of photovoltaic modules using electroluminescence (EL) images was proposed in Zhao et al. (2023). The algorithm used high-resolution network (HRNet) and a self-fusion network (SeFNet) for better feature fusion and classification accuracy.

What is PVL-AD dataset for photovoltaic panel defect detection?

To meet the data requirements, Su et al. 18 proposed PVEL-AD dataset for photovoltaic panel defect detection and conducted several subsequent studies 19, 20, 21 based on this dataset. In recent years, the PVEL-AD dataset has become a benchmark for photovoltaic (PV) cell defect detection research using electroluminescence (EL) images.