Work

Automated fault detection and analysis for large photovoltaic systems using photovoltaic module fault detection in drone vision system

Year: 2025

Type: article

Abstract: This study presents an innovative approach to fault detection in large-scale photovoltaic (PV) systems by leveraging the capabilities of drones and machine vision technologies. The proposed method is unsupervised, eliminating the need for manual intervention in identifying and analyzing faults in PV installations. By employing drone vision techniques equipped with high-resolution cameras and advanced image processing algorithms, comprehensive visual data of solar panels were captured. The collected images were processed for automatic detection and classification of various faults such as cracks, hotspots, and shading issues. The integration of these technologies not only enhances the accuracy of fault detection but also significantly reduces the time and cost associated with traditional inspection methods. This approach ensures the efficient and reliable operation of PV systems, contributing to the sustainable generation of solar energy. The original dataset employed in this work can be found at https://doi.org/10.17632/5ssmfpgrpc.1.

Source: Edelweiss Applied Science and Technology

DOI: 10.55214/25768484.v9i2.4542

Authors: Rotimi-Williams Bello, Pius A. Owolawi, Chunling Tu, Etienne A. van Wyk

Institution: Tshwane University of Technology

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: diamond

Funder: Tshwane University of Technology