Photovoltaic panel fine grid line defects

What is grid-connected PV fault diagnosis?

Comprehensive grid-connected PV fault diagnosis: Unlike contemporary works, the developed fault diagnosis model addresses various faults across the entire grid-connected PV system, including PV array faults, boost converter issues, power inverter malfunctions, and grid anomalies.

What are failures & defects in PV systems?

Failures & Defects in PV Systems: Typical Methods for Detecting Defects and Failures Generally,any effect on the PV module or device which decreases the performance of the plant, or even influences the module characteristics, is considered a failure. A defect is an unexpected or unusual happening which was not observed on the PV plant before.

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.

What are the different types of defects in PV panels?

As illustrated in Fig. 1, the common types of defects in PV panels include crack, finger interruption, black core, thick line, star crack, corner, horizontal dislocation, vertical dislocation, and short circuit often accompanied by complex background interference. However, defect detection in EL images requires highly specialized knowledge.

Can infrared imaging detect defects in photovoltaic cells and panels?

Using Synchronized Thermography and Time-Resolved Thermography techniques, the authors locate the Region of Interest in external environments in an infrared image dataset to detect defects in photovoltaic (PV) cells and panels ( Schuss et al., 2020, El-Amiri et al., 2018 ).

What are the challenges of defect detection in PV systems?

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.