Automatic arrangement of photovoltaic panels by drones

Can autonomous drones detect faulty PV modules?

To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station.

Can uav photogrammetry be used for Autonomous inspection of PV plants?

The autonomous inspection of PV plants through UAV photogrammetry has been explored in the literature , , , . The UAV is given a set of waypoints, usually arranged in such a way to cover a delimited area to ensure the required horizontal and vertical overlapping of images.

How are aerial and ground data used in a solar PV system?

In their study, aerial data were taken using a UAV drone, collecting RGB images to build an orthophoto of the PV system and used it as an interactive map in the GIS application. In addition, thermal photos were captured and reviewed using ThermoViewer. On the other hand, ground data were acquired with I–V curve tests.

Can drones be used for PV power station monitoring?

With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations.

Can a drone fly autonomously over a planned flight path?

The proposed drone system can fly autonomously over an automatically planned flight path by our flight planning algorithm. As automatic hot-spot localization is one of the essential aspects of PV plant inspection, our system can estimate the exact GPS location of the faulty PV modules among thousands of PV modules.

Does automatic drone flight path planning eliminate manual drone control?

In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea.