Inspecting Mega Solar Plants Through Computer Vision And Drone Technologies

Abstract

This research introduces an innovative method for
monitoring large-scale grid-connected photovoltaic
modules in solar power plants using the advanced
YOLO v5 object detection algorithm alongside
traditional image processing techniques. We
emphasize a crucial component of an automated
system where a drone autonomously surveys the solar
park and captures video footage. Our trained
YOLOv5 identifies clean and dirty panels effectively.
This process is tailored for a specific site and will be
implemented using a Raspberry Pi. The system
processes drone-captured images, generates reports,
and automatically emails them to the relevant
department daily, ensuring timely maintenance for
the longevity and safe operation of solar arrays.
While the project is straightforward, it stands out due
to its use of cutting-edge technologies for inspecting
large solar power plants. The inspection time has
been reduced from approximately one hundred and
twenty hours to just five minutes, resulting in a time
savings of 99.93% through advanced vision and
robust automation techniques.