Advertisement

3D Research

, 10:3 | Cite as

IoT Based Framework: Mathematical Modelling and Analysis of Dust Impact on Solar Panels

  • Alisha Makkar
  • Anisha RahejaEmail author
  • Rashmi Chawla
  • Shailender Gupta
3DR Express
  • 20 Downloads
Part of the following topical collections:
  1. Multimedia tools

Abstract

The solar photovoltaic performance is governed by manifold parameters viz. temperature, irradiance, dust on solar module, photoactive material, panel orientation. Among these dust is a critical impediment, as its accumulation on panel surface degrades its productivity; while frequent cleaning sessions affect module’s life and result into PV destruction. Accordingly, the need to know dust thickness responsible for deteriorating panel’s capability and adequate cleaning time of solar panels to produce optimum yields is requisite. This paper aims to discern a right cleaning time, owing to a particular dust thickness so as to conserve the panel efficiency using internet of things (IoT). The mathematical correlations of PV efficiency and current with thickness of accumulated dust are derived using linear regression. Further, these equations are associated with an IoT-based platform which remotely monitors and records PV output current; thereafter dust thickness corresponding to a significant current reduction is estimated. For this, experimental data of 46 inverters with total 114,819.30 kWh productions in a month with an average of 4416.13 kWh/day is accessed and the results pertaining to mathematical analysis exhibit a decline in current by 1 A with 5.51 × 10−3 mm thickness of dust.

Keywords

Dust deposition Output efficiency Photovoltaic module Transmittance 

Notes

Acknowledgements

The authors would like to thank Mr. Gourav Kumar Soni, Hardware Design Engineer, AdvanceTech India Pvt. Ltd for his kind support. We are grateful to IIT Delhi for the resourceful data.

References

  1. 1.
    IEA—Key world energy statistics. (2015). https://www.iea.org/weo2017, Accessed February 21, 2018.
  2. 2.
    Physical Progress (Achievements). Ministry of New & Renewable Energy. Retrieved 18 July 2018.Google Scholar
  3. 3.
    Kayes, B. M., et al. (2011). 27.6% Conversion efficiency, a new record for single-junction solar cells under 1 sun illumination. In Photovoltaic specialists conference (PVSC) IEEE 37th, (pp. 4–8).Google Scholar
  4. 4.
    Green, M. A., Emery, K., Hishikawa, Y., Warta, W., & Dunlop, E. D. (2017). Solar cell efficiency tables (Version 49). Progress in Photovoltaics: Research and Applications, 25(1), 3–13.CrossRefGoogle Scholar
  5. 5.
    Kayes, B. M., Nie, H., Twist, R., Spruytte, S. G., Reinhardt, F., Kizilyalli, I. C., Higashi, G. S. (2011). 27.6% conversion efficiency, a new record for single-junction solar cells under 1 sun illumination. In Proceedings of the 37th IEEE Photovoltaic Specialists Conference.Google Scholar
  6. 6.
    Geisthardt, R. M., Topic, M., & Sites, J. R. (2015). Status and potential of CdTe solar-cell efficiency. IEEE Journal of Photovoltaics, 5(4), 1217–1221.CrossRefGoogle Scholar
  7. 7.
    Ramanujam, J., & Singh, U. P. (2017). Copper indium gallium selenide based solar cells—A review. Energy & Environmental Science, 10(6), 1306–1319.CrossRefGoogle Scholar
  8. 8.
    Zheng, Z., et al. (2016). Recent progress towards quantum dot solar cells with enhanced optical absorption. In Nano science letters.Google Scholar
  9. 9.
    MIT Energy Initiative. (2015). The future of solar energy: An interdisciplinary MIT study. In Chapter 2: “Photovoltaic Technology” (pp. 21–45).Google Scholar
  10. 10.
    Current and future costs of photovoltaics: Long-term scenarios for market development, system prices and LCOE of utility-scale PV systems (Fraunhofer Institute for Solar Energy Systems, 2015). http://go.nature.com/2aYJCgc.
  11. 11.
    Green, M. A. (2016). Commercial progress and challenges for photovoltaics. Nature Energy, 1, 15015.CrossRefGoogle Scholar
  12. 12.
    Green, M. A., Ho-Baillie, A., & Snaith, H. J. (2014). The emergence of perovskite solar cells. Nature Photonics, 8, 506–514.CrossRefGoogle Scholar
  13. 13.
    Albrecht, S., et al. (2016). Monolithic perovskite/silicon-heterojunction tandem solar cells processed at low temperature. Energy & Environmental Science, 9, 81–88.CrossRefGoogle Scholar
  14. 14.
    Kim, J. P., Lim, H., Song, J. H., Chang, Y. J., & Jeon, C. H. (2011). Numerical analysis on the thermal characteristics of photovoltaic module with ambient temperature variation. Solar Energy Materials and Solar Cells, 95, 404–407.CrossRefGoogle Scholar
  15. 15.
    Durisch, et al. (2003). Small thermophotovoltaic prototype systems. Solar Energy, 75, 11–15.CrossRefGoogle Scholar
  16. 16.
    Chawla, R., Singal, P., & Garg, A. K. (2018). A Mamdani fuzzy logic system to enhance solar cell micro-cracks image processing. 3D Research, 9, 34.  https://doi.org/10.1007/s13319-018-0186-7.CrossRefGoogle Scholar
  17. 17.
    Guan, Y., et al. (2017). In-situ investigation of the effect of dust deposition on the performance of polycrystalline silicon photovoltaic modules. Renewable Energy, 101, 1273–1284.CrossRefGoogle Scholar
  18. 18.
    Rajput, A. S., et al. (2018). Quantitative estimation of electrical performance parameters of individual solar cells in silicon photovoltaic modules using electroluminescence imaging. Solar Energy, 173, 201–208.CrossRefGoogle Scholar
  19. 19.
    Maghami, M. R., et al. (2016). Power loss due to soiling on solar panel: A review. Renewable and Sustainable Energy Reviews, 59, 1307–1316.CrossRefGoogle Scholar
  20. 20.
    Paudyal, B. R., et al. (2016). Dust accumulation effects on efficiency of solar PV modules for off grid purpose: A case study of Kathmandu. Solar Energy, 135, 103–110.CrossRefGoogle Scholar
  21. 21.
    Zanella, A. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22–32.CrossRefGoogle Scholar
  22. 22.
    Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 1(2), 112–121.CrossRefGoogle Scholar
  23. 23.
    Kishore, P., et al. (2017). Internet of things based low-cost real-time home automation and smart security system. International Journal of Advanced Research in Computer and Communication Engineering, 6, 505–509.CrossRefGoogle Scholar
  24. 24.
    Semaoui, S., Arab, A. H., Boudjelthi, E. K., Bacha, S., & Zeraia, H. (2015). Dust effect on optical transmittance of photovoltaic module glazing in a desert region. Energy Procedia, 74, 1347–1357.CrossRefGoogle Scholar
  25. 25.
    Said, S. A., et al. (2014). Fundamental studies on dust fouling effects on PV module performance. Solar Energy, 107, 328–337.CrossRefGoogle Scholar
  26. 26.
    Vivar, M., et al. (2010). Effect of soiling in CPV systems. Solar Energy, 84, 1327–1335.CrossRefGoogle Scholar
  27. 27.
    Ghazi, S., et al. (2014). Dust effect on flat surfaces-a review paper. Renewable and Sustainable Energy Reviews, 33, 742–751.CrossRefGoogle Scholar
  28. 28.
    Kalogirou, S. A., Agathokleous, R., & Panayiotou, G. (2013). On-site PV characterization and the effect of soiling on their performance. Energy, 51(8), 439–446.CrossRefGoogle Scholar
  29. 29.
    Darwish, Z. A., Kazem, H. A., Sopian, K., Al-Goul, M., & Alawadhi, H. (2015). Effect of dust pollutant type on photovoltaic performance. Renewable and Sustainable Energy Reviews, 41, 735–744.CrossRefGoogle Scholar
  30. 30.
    Abderrezek, M., et al. (2017). Experimental study of the dust effect on photovoltaic panels’ energy yield. Solar Energy, 142, 308–320.CrossRefGoogle Scholar
  31. 31.
    Sayyah, A., et al. (2014). Energy yield loss caused by dust deposition on photovoltaic panels. Solar Energy, 107, 576–604.CrossRefGoogle Scholar
  32. 32.
    Al-Hasan, A. Y., et al. (2005). A new correlation between photovoltaic panel’s efficiency and amount of sand dust accumulated on their surface. International Journal of Sustainable Energy, 24, 187–197.CrossRefGoogle Scholar
  33. 33.
    Rao, A., Pillai, R., Mani, M., & Ramamurthy, P. (2014). Influence of dust deposition on photovoltaic panel performance. Energy Procedia, 54, 690–700.CrossRefGoogle Scholar
  34. 34.
    Kaldellis, J., et al. (2011). Simulating the dust effect on the energy performance of photovoltaic generators based on experimental measurements. Energy, 36, 5154–5161.CrossRefGoogle Scholar
  35. 35.
    Hegazy, A. A. (2001). Effect of dust accumulation on solar transmittance through glass covers of plate-type collectors. Renewable Energy, 22, 525–540.CrossRefGoogle Scholar
  36. 36.
    Klugmann-Radziemska, E. (2015). Degradation of electrical performance of a crystalline photovoltaic module due to dust deposition in northern Poland. Renewable Energy, 78, 418–426.CrossRefGoogle Scholar
  37. 37.
    Hammad, B., et al. (2018). Modelling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study. Renewable and Sustainable Energy Reviews, 82, 2218–2234.CrossRefGoogle Scholar
  38. 38.
    Gholami, A., et al. (2017). Experimental study of factors affecting dust accumulation and their effects on the transmission coefficient of glass for solar applications. Renewable Energy, 112, 466–473.CrossRefGoogle Scholar
  39. 39.
    Naeem, M., et al. (2015). Cleaning frequency optimization for soiled photovoltaic modules. In 2015 IEEE 42nd photovoltaic specialist conference (PVSC), New Orleans, LA (pp. 1–5).  https://doi.org/10.1109/pvsc.2015.7355972.
  40. 40.
    Mei, H., et al. (2016). Study on cleaning frequency of grid-connected PV modules based on Related Data Model. In 2016 IEEE international conference on power and renewable energy (ICPRE), Shanghai (pp. 621–624).  https://doi.org/10.1109/icpre.2016.7871152.
  41. 41.
    Mani, M., et al. (2010). Impact of dust on solar photovoltaic (PV) performance: Research status, challenges and recommendations. Renewable and Sustainable Energy Reviews, 14, 3124–3131.CrossRefGoogle Scholar
  42. 42.
    Schultz, O., et al. (2004). Multi crystalline silicon solar cells exceeding 20% efficiency. Progress in Photovoltaics: Research and Applications, 12, 553–558.CrossRefGoogle Scholar
  43. 43.
    Siddiqui, R., & Bajpai, U. (2012). Correlation between thicknesses of dust collected on photovoltaic module and difference in efficiencies in composite climate. International Journal of Energy and Environmental Engineering, 3, 26.CrossRefGoogle Scholar

Copyright information

© 3D Display Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Alisha Makkar
    • 1
  • Anisha Raheja
    • 1
    Email author
  • Rashmi Chawla
    • 1
  • Shailender Gupta
    • 1
  1. 1.Department of Electronics EngineeringJ.C. Bose University of Science and Technology, YMCAFaridabadIndia

Personalised recommendations