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

  • Séra, Dezso (PI)
  • Spataru, Sergiu Viorel (Project Participant)
  • Parikh, Harsh Rajesh (Project Participant)

Project Details

Description

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.
Short titleDronEL
StatusFinished
Effective start/end date01/01/201731/12/2019

Funding

  • Innovation Fund Denmark: DKK19,993,065.00

Fingerprint

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    • Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

      Parikh, H. R., Buratti, Y., Spataru, S. V., Villebro, F., A. dos Reis Benatto, G., Poulsen, P., Wendlandt, S., Kerekes, T., Séra, D. & Hameiri, Z., Dec 2020, In: Applied Sciences. 10, 24, p. 1-15 15 p.

      Research output: Contribution to journalJournal articleResearchpeer-review

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    • 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, p. 0538-0543 6 p. 0160-8371. (I E E E Photovoltaic Specialists Conference. Conference Record).

      Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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      1 Citation (Scopus)
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    • Machine learning prediction of defect types for electroluminescence images of photovoltaic panels

      Mantel, C., Villebro, F., Alves dos Reis Benatto, G., Parikh, H. R., Wendlandt, S., Hossain, K., Poulsen, P., Spataru, S. V., Séra, D. & Forchhammer, S., Sep 2019, Proceedings of SPIE Optical Engineering + Applications: Applications of Machine Learning. Zelinski, M. E., Taha, T. M., Howe, J., Awwal, A. A. S. & Iftekharuddin, K. M. (eds.). SPIE - International Society for Optical Engineering, 9 p. 1113904. (Proceedings of SPIE, the International Society for Optical Engineering, Vol. 11139).

      Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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      2 Citations (Scopus)
      13 Downloads (Pure)