curve of the solar panel. Analysis of its variations aids in defect determination. However, this method demands measuring each individual photovoltaic panel, a task impracticable due to
In this paper, we propose an image processing and control system that can automatically adjust the gimbals for PV module inspection using autonomous drones. The proposed system consists of a dual camera, a gimbal frame, a
Nevertheless, it was estimated that 80% of the roof surfaces would be geometrically suitable for allocating PV panels, which meant that a total area of 17,000m 2 of solar panels could be fitted on the roofs in the sampled
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.
Therefore, the drone has to be placed at a closer distance from the PV module. In that case, since we cannot see the whole picture of the module, it is a prerequisite to take the picture in the center of our target object.
The objective of this research is to compare the fault detection analyses performed, for two different solar PV plants, using alternatively an unmanned drone and a manned aircraft as aerial platforms, equipped with different IR cameras to provide reliable and comparable thermal images over the same inspected sites.
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.
The autonomous inspection of PV plants through UAV photogrammetry has been explored in the literature [ 14, 15, 29, 30 ]. The UAV is given a set of waypoints, usually arranged in such a way as to cover a delimited area to ensure the required horizontal and vertical overlapping of images.
Electroluminescence (EL) imaging of photovoltaic (PV) solar panels provides high accuracy in detecting defects and faults, such as cracks, broken cells, interconnections, shunts, among many others; furthermore, the EL technique is used extensively due to a high level of detail and direct relationship to injected carrier density.
