DronEL - Fast and accurate inspection of large photovoltaic plants using aerial drone imaging

  • Séra, Dezso (PI (principal investigator))
  • Spataru, Sergiu Viorel (Projektdeltager)
  • Parikh, Harsh Rajesh (Projektdeltager)



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.
Kort titelDronEL
Effektiv start/slut dato01/01/201731/12/2019


  • Innovationsfonden: kr 19.993.065,00

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  • Publikation

    • 13 Konferenceartikel i proceeding
    • 2 Tidsskriftartikel
    • 1 Konferenceabstrakt til konference

    Automatic Fault detection through Supervised Machine Learning Techniques using histogram and extracted statistical parameters

    Parikh, H. R., Buratti, Y., Spataru, S. V., A. dos Reis Benatto, G., Poulsen, P., Mantel, C., Séra, D. & Hameiri, Z., 2020, (Under udarbejdelse) I : Progress in Photovoltaics: Research and Applications. 6 s.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  • Enhancement analysis of Electroluminescence images for fault detection in photovoltaic panels under ambient light and outdoor condition

    Parikh, H. R., Spataru, S. V., A. dos Reis Benatto, G., Mantel, C. & Séra, D., 2020, (Under udarbejdelse) I : I E E E Journal of Photovoltaics. 6 s.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  • A Photovoltaic Module Diagnostic Setup for Lock-IN Electroluminescence Imaging

    Parikh, H. R., Spataru, S. V., Séra, D., A. dos Reis Benatto, G., Poulsen, P., Mantel, C., Forchhammer, S., Larsen, M., Frederiksen, K. H. B. & Vedde, J., jun. 2019, Proceedings of 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC). IEEE Press, 6 s. (I E E E Photovoltaic Specialists Conference. Conference Record).

    Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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    Dronebåret elektroluminisens sikrer hurtig og præcis inspektion af solcelleanlæg

    Sergiu Viorel Spataru


    1 Mediebidrag


    Droner skal overvåge solcelleanlæg


    12 elementer af Mediedækning