Evaluation of Photovoltaic Systems Performance Using Satellites and Drones Digital Imaging

  • Chapter
  • First Online:
Technical and Technological Solutions Towards a Sustainable Society and Circular Economy

Abstract

Due to changing lifestyle and population growth, the demand for energy is increasing at an unprecedented rate, leading to an increase of greenhouse gas emissions caused by conventional energies. Renewable energies, more particularly photovoltaic energy have been frequently used following the strategy proposed by the Moroccan government. Certainly, this technology is considered an environmental solution but its suitability in the field still poses performance challenges. This is where the use of remote sensing and GIS tools makes it possible to select the optimal area, monitor the installed photovoltaic panels, and evaluate their performances. This study aims to evaluate the performance of photovoltaic generators using digital imaging. In fact, there is a possibility of improving precision of identifying PV generators by switching from satellite images to have digital information that can evaluate performances according to importance of sensing the thermal behavior and reflectance from panel to panel. Remote sensing is used for evaluating the presence, absence and performance of photovoltaic panels. It can collect data on photovoltaic system using satellite images to rightly choose location and orientation for photovoltaic panels and evaluate their general state. This is done by controlling solar irradiation, monitoring panel temperature, detecting module fouling, managing vegetation and monitoring water levels. However, satellite images cannot be used to precisely take images as done by drone thermal imaging offering greater flexibility and higher spatial resolution from different angles of close distance. In fact, evaluation of photovoltaic panels’ performance using drone imagery enables individual panel dysfunctions to be detected, making it simple to resolve these problems in a real time and hel** to guarantee system sustainability by minimizing cost and time charges involved for PV systems maintenance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 160.49
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 213.99
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. van Ruijven, B.J, De Cian E, Sue Wing: Amplification of future energy demand growth due to climate change. Nat. Commun. (2019)

    Google Scholar 

  2. Ambrose, J.: Greenhouse gas emissions from global energy industry still rising—report. Energy industry (2023)

    Google Scholar 

  3. Peplow, M.: A new kind of solar cell is coming: is it the future of green energy? Nature (2023)

    Google Scholar 

  4. Atasu, A., Duran, S., Van Wassenhove, L.N.: The dark side of solar power as interest in clean energy surges, used solar panels are going straight into landfill. Sustain. Bus. Pract (2021)

    Google Scholar 

  5. Chadburn, B.: 5 common challenges with remote sensing and how to tackle them (2020)

    Google Scholar 

  6. Sahbeni, G., Ngabire, M., Musyimi, P.K., Székely, B.: Challenges and opportunities in remote sensing for soil salinization map** and monitoring: a review. MDPI (2023)

    Google Scholar 

  7. Oke, O., Akindele, S.O.: Challenges and prospects of remote sensing and GIS technology for forest resources management in Nigeria. RESEARCHGATE (2022)

    Google Scholar 

  8. Chen, Q., Li, X., Zhang, Z., Zhou, C., Guo, Z., Liu, Z., Zhang, H.: Remote sensing of photovoltaic scenarios: techniques, applications and future directions. Appl. Energy (2023)

    Google Scholar 

  9. Chen, Q., Li, X., Zhang, Z., Zhou, C., Guo, Z., Liu, Z., Zhang, H.: Remote sensing of photovoltaic scenarios: techniques, applications and future directions. Appl. Energy (2023)

    Google Scholar 

  10. Li, P., Zhang, H., Guo, Z., Lyu, S., Chen, J., Li, W., et al.: Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning. Adv. Appl. Energy (2021)

    Google Scholar 

  11. Wang, Z., Wang, Z., Majumdar, A., Rajagopal, R.: Identify solar panels in low resolution satellite imagery with Siamese architecture and cross-correlation (2017)

    Google Scholar 

  12. Yu, J., Wang, Z., Majumdar, A., Rajagopal, R.: DeepSolar: a machine learning framework to efficiently construct a solar deployment database in the United States. Joule (2018)

    Google Scholar 

  13. Stowell, D., Kelly, J., Tanner, D., Taylor, J., Jones, E., Geddes, J., et al.: A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK. Sci. Data (2020)

    Google Scholar 

  14. Kruitwagen, L., Story, K.T., Friedrich, J., Byers, L., Skillman, S., Hepburn, C.: A global inventory of photovoltaic solar energy generating units. Nature (2021)

    Google Scholar 

  15. Ko, L., Wang, J.-C., Chen, C.-Y., Tsai, H.-Y.: Evaluation of the development potential of rooftop solar photovoltaic in Taiwan. Renew. Energy (2015)

    Google Scholar 

  16. Mainzer, K., Killinger, S., McKenna, R., Fichtner, W.: Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques. Sol. Energy (2016)

    Google Scholar 

  17. Ates, A.M., Yilmaz, O.S., Gulgen, F.: Using remote sensing to calculate floating photovoltaic technical potential of a dam’s surface. Sustain. Energy Technol. Assess. (2020)

    Google Scholar 

  18. Zhang, T., Li, Z., Jiang, H., Luo, Y., Xu, S.: Deep learning method for evaluating photovoltaic potential of urban land-use: a case study of Wuhan, China. Appl. Energy (2021)

    Google Scholar 

  19. Zhang, G., Cerra, D., Müller, R.: Shadow detection and restoration for hyperspectral images based on nonlinear spectral unmixing. Remote Sensing (2020)

    Google Scholar 

  20. Roper, T., Andrews, M.: Shadow modelling and correction techniques in hyperspectral imaging, the institution of engineering and technology (2013)

    Google Scholar 

  21. Supe, H., Avtar, R., Singh, D., Ravankar, A.A., Mohan, G., Chander, K.S., Tutubalina, O., Kharraz, A.: Google earth engine for the detection of soiling on photovoltaic solar panels in arid environments. Remote Sensing (2020)

    Google Scholar 

  22. Ji, C., Bachmann, M., Esch, T., Zeidler, J.: Solar photovoltaic module detection using laboratory and airborne imaging spectroscopy data. Remote Sensing Environ. (2021)

    Google Scholar 

  23. Ali, M.U., Khan, H.F., Zafar, A.: A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography. Solar Energy (2020)

    Google Scholar 

  24. Wang, F., Wang, Z., Chen, Z., Zhu, D., Cong, W.: An edge-guided deep learning solar panel hotspot thermal image segmentation algorithm. Appl. Sci. (2023)

    Google Scholar 

  25. Dotenco, S., Dalsass, M., Winkler, L., Würzner, T., Brabec, C., Maier, A., et al.: Automatic detection and analysis of photovoltaic modules in aerial infrared imagery. In: 2016 IEEE Winter Conference on Applications of Computer Vision. IEEE (2016)

    Google Scholar 

  26. Shen, H., Zhu, L., Hong, X., Chang, W.: ROI extraction method of infrared thermal image based on GLCM characteristic imitate gradient. Commun. Comput. Inf. Sci. 771. Springer (2017)

    Google Scholar 

  27. Camilo, J., Wang, R., Collins, L.M., Bradbury, K., Malof, J.M.: Application of a semantic segmentation convolutional neural network for accurate automatic detection and map** of solar photovoltaic arrays in aerial imagery (2018)

    Google Scholar 

  28. Jie, Y., Ji, X., Yue, A., Chen, J., Deng, Y., Chen, J., et al.: Combined multi-layer feature fusion and edge detection method for distributed photovoltaic power station identification. Energies (2020)

    Google Scholar 

  29. Rausch, B., Mayer, K., Arlt, M.-L., Gust, G., Staudt, P., Weinhardt, C., et al.: An enriched automated PV registry: combining image recognition and 3D building data (2020)

    Google Scholar 

  30. Mayer, K., Rausch, B., Arlt, M.L., Gust, G., Wang, Z., Neumann, D., et al.: 3D-PV-locator: large-scale detection of rooftop-mounted photovoltaic systems in 3D. Appl. Energy (2022)

    Google Scholar 

  31. Mainzer, K., Killinger, S., McKenna, R., Fichtner, W.: Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques. Sol. Energy (2017)

    Google Scholar 

  32. Nadal, A., Alamús, R., Pipia, L., Ruiz, A., Corbera, J., Cuerva, E., et al.: Urban planning and agriculture. Methodology for assessing rooftop greenhouse potential of nonresidential areas using airborne sensors. Sci. Total Environ. (2017)

    Google Scholar 

  33. Krapf, S., Kemmerzell, N., Uddin, S.K.H., Vazquez, M.H., Netzler, F., Lienkamp, M.: Towards scalable economic photovoltaic potential analysis using aerial images and deep learning. Energies (2021)

    Google Scholar 

  34. King, D.L., Kratochvil, J.A., Quintana, M.A., Mcmahon TJ. Applications for infrared imaging equipment in photovoltaic cell, module, and system testing

    Google Scholar 

  35. Kaplani, E.: Detection of degradation effects in field-aged c-Si solar cells through IR thermography and digital image processing. Int. J. Photoenergy (2012)

    Google Scholar 

  36. Quarter, P.B., Grimaccia, F., Leva, S., Mussetta, M., Aghaei, M.: Light unmanned aerial vehicles (UAVs) for cooperative inspection of PV plants. IEEE J. Photovolt. (2014)

    Google Scholar 

  37. Tsanakas, J.A., Chrysostomou, D., Botsaris, P.N., Gasteratos, A.: Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements. Int. J. Sustain. Energy (2015)

    Google Scholar 

  38. Gao, X., Munson, E., Abousleman, G.P., Si, J.: Automatic solar panel recognition and defect detection using infrared imaging. In: Autom. (2015)

    Google Scholar 

  39. Kim, D., Youn, J., Kim, C.: Automatic detection of malfunctioning photovoltaic modules using unmanned aerial vehicle thermal infrared images. J. Korean Soc. Surv. Geod Photogramm Cartogr. (2016)

    Google Scholar 

  40. Jaffery, Z.A., Dubey, A.K., Irshad, H.A.: Scheme for predictive fault diagnosis in photovoltaic modules using thermal imaging. Infrared Phys. Technol. (2017)

    Google Scholar 

  41. Aghaei, M., Leva, S., Grimaccia, F.: PV power plant inspection by image mosaicing techniques for IR real-time images (2016)

    Google Scholar 

  42. Montanez, L.E., Valentín-Coronado, L.M., Moctezuma, D., Flores, G.: Photovoltaic module segmentation and thermal analysis tool from thermal images (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Karima Laaroussi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Laaroussi, K., Jemjami, S., Harkani, A., Benabdelouahab, T., Moufti, A., El Aissaoui, A. (2024). Evaluation of Photovoltaic Systems Performance Using Satellites and Drones Digital Imaging. In: Mabrouki, J., Mourade, A. (eds) Technical and Technological Solutions Towards a Sustainable Society and Circular Economy. World Sustainability Series. Springer, Cham. https://doi.org/10.1007/978-3-031-56292-1_18

Download citation

Publish with us

Policies and ethics

Navigation