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Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems

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  • Bressan, M.
  • Gutierrez, A.
  • Garcia Gutierrez, L.
  • Alonso, C.

Abstract

This work presents an emulation in real-time of the shaded PV systems with a hot-spot prevention. The PV model takes into account the photo-induced current contributions from unshaded and shaded sides thanks to parameters such as the shadow transmittance and the percentage area of the shadows. The use of shadow fault detection in real time is employed avoiding all form of hot-spot formation and PV cells power dissipation. The calculation uses a simple derivative equation able to give the area of detection in function of the PV module voltage. The implementation of the emulator in FPGA takes advantages as a result of their features of adaptability and parallel processing suitable for emulation of complex shading visible on PV systems. The emulation of the proposed PV model and the hot-spot prevention are validated through two experimental tests on PV modules.

Suggested Citation

  • Bressan, M. & Gutierrez, A. & Garcia Gutierrez, L. & Alonso, C., 2018. "Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems," Renewable Energy, Elsevier, vol. 127(C), pages 334-343.
    • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:334-343
    DOI: 10.1016/j.renene.2018.04.045
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    1. Ko, Suk Whan & Ju, Young Chul & Hwang, Hye Mi & So, Jung Hun & Jung, Young-Seok & Song, Hyung-Jun & Song, Hee-eun & Kim, Soo-Hyun & Kang, Gi Hwan, 2017. "Electric and thermal characteristics of photovoltaic modules under partial shading and with a damaged bypass diode," Energy, Elsevier, vol. 128(C), pages 232-243.
    2. Chine, W. & Mellit, A. & Pavan, A. Massi & Kalogirou, S.A., 2014. "Fault detection method for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 66(C), pages 99-110.
    3. Wang, Yaw-Juen & Hsu, Po-Chun, 2011. "An investigation on partial shading of PV modules with different connection configurations of PV cells," Energy, Elsevier, vol. 36(5), pages 3069-3078.
    4. Silvestre, S. & Boronat, A. & Chouder, A., 2009. "Study of bypass diodes configuration on PV modules," Applied Energy, Elsevier, vol. 86(9), pages 1632-1640, September.
    5. d'Alessandro, Vincenzo & Di Napoli, Fabio & Guerriero, Pierluigi & Daliento, Santolo, 2015. "An automated high-granularity tool for a fast evaluation of the yield of PV plants accounting for shading effects," Renewable Energy, Elsevier, vol. 83(C), pages 294-304.
    6. Bressan, M. & El Basri, Y. & Galeano, A.G. & Alonso, C., 2016. "A shadow fault detection method based on the standard error analysis of I-V curves," Renewable Energy, Elsevier, vol. 99(C), pages 1181-1190.
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    Cited by:

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    5. Wang, Haoxuan & Chen, Huaian & Wang, Ben & Jin, Yi & Li, Guiqiang & Kan, Yan, 2022. "High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network," Applied Energy, Elsevier, vol. 318(C).
    6. Koo Lee & Sung Bae Cho & Junsin Yi & Hyo Sik Chang, 2022. "Simplified Recovery Process for Resistive Solder Bond (RSB) Hotspots Caused by Poor Soldering of Crystalline Silicon Photovoltaic Modules Using Resin," Energies, MDPI, vol. 15(13), pages 1-19, June.
    7. Wu, Jing & Zhang, Ling & Liu, Zhongbing & Wu, Zhenghong, 2021. "Coupled optical-electrical-thermal analysis of a semi-transparent photovoltaic glazing façade under building shadow," Applied Energy, Elsevier, vol. 292(C).
    8. Yu, Qiongwan & Hu, Mingke & Li, Junfei & Wang, Yunyun & Pei, Gang, 2020. "Development of a 2D temperature-irradiance coupling model for performance characterizations of the flat-plate photovoltaic/thermal (PV/T) collector," Renewable Energy, Elsevier, vol. 153(C), pages 404-419.

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