DronEL – A fast and accurate inspection of large photovoltaic plants using aerial drone imaging.
THE PURPOSE of this DronEL project is to develop and bring to market an aerial drone based automated solution (DronEL) used for a full PV plant survey for more accurate survey in less time. The automatic drone-based inspection method combines IR, EL and PL imaging, and visual images.
THE COMMERCIAL POTENTIAL OF DRONEL will
be exploited primarily by Sky-Watch and Kenergy. Sky-Watch will be the manufacturer of IR/PL/VL equipped drones, while Kenergy will sell them and provide training to PV plant operators. Furthermore, Kenergy will also provide O&M services using the DronEL platform.
Photovoltaic systems (PV) are now a major electricity supplier in many countries around the world, reaching 227 GW instal-led capacity in 2015 and is ex-pected to surpass 756 GW i 2025.
The PV service market is estimated to 40 billion DKK in 2020 and this project aim at tapping into this by exploiting excellent research together with leading industry players in synergetic fields.
(As shown on DronEL’s project page on Aalborg University’s website)1
Fault detection and regular maintenance of both small and large PV installations, is important to secure the expected ROI, however the frequency and detail-level are limited by cost of manpower. Fast PV plant inspection, based on drone-mounted infrared (IR) cameras, reduces the inspection time and cost significantly, and is an emerging alternative to traditional methods. However, the detection accuracy of IR is limited by weather conditions, and to faults causing a sufficient increase in temperature.
This project – DronEL, will develop a fast and accurate automatic drone-based inspection system for PV plants that combines IR, luminescence (EL or PL) imaging, and visual images (VI). The system will be able to detect a wider range of PV panel failures: visual defects, hot-spots, solar cell cracks, potential-induced degradation, and more.
DronEL is a significant leap forward in PV plant inspection technology, combining the speed of drone-based IR inspection with the in-depth analysis of EL/PL. The project will carry out R&D activities in three main areas: Image acquisition and processing, Image interpretation – correlating images with known PV fault types, and drone control system and deployment. Accordingly, the project consists of the following main tasks: (i) R&D of suitable PL/EL imaging techniques. (ii) Integration and optimization of the imaging system on a drone (iii) Development of IR, PL/EL, and VI analysis for automatic fault detection and identification (iv) Integration and test of the drone system with the image analysis and automatic fault detection.