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A dynamic simulation platform for fault modelling and characterisation of building integrated photovoltaics

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  • Lin, Wenye
  • Ma, Zhenjun
  • Li, Kehua
  • Tyagi, V.V.
  • Pandey, A.K.

Abstract

This paper presents the development and validation of a building integrated photovoltaic (BIPV) simulation platform which can introduce and characterise main faults of BIPV systems. The coupling of the electrical circuit with thermal balance, and the introduction of different faults and their interactions with broken/active protection diodes are among the main contributions of this study. Through comparison with the data measured from a real BIPV installation, it was found that this platform is able to provide an acceptable prediction of the performance of BIPV systems. The design and test of a series of scenarios demonstrated that this platform was capable to simulate and characterise dynamic faulty operations of BIPV systems influenced by various faults. It was found that short-circuit current, gradient information and inflection point in the current-voltage (I–V) curve can be utilised to discriminate the aging and medium partial shading faults with corresponding broken/active bypass diodes. The open-circuit voltage and inflection point in the I–V curve, and the PV cell temperature can be used to distinguish the short-circuit fault in a PV string and inter-string short-circuit fault, with corresponding broken/active blocking diodes. Hotspot phenomenon can also be observed if a minor partial shading fault was introduced in the simulation.

Suggested Citation

  • Lin, Wenye & Ma, Zhenjun & Li, Kehua & Tyagi, V.V. & Pandey, A.K., 2021. "A dynamic simulation platform for fault modelling and characterisation of building integrated photovoltaics," Renewable Energy, Elsevier, vol. 179(C), pages 963-981.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:963-981
    DOI: 10.1016/j.renene.2021.07.035
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    References listed on IDEAS

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    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Fabrizio Bonacina & Alessandro Corsini & Lucio Cardillo & Francesca Lucchetta, 2019. "Complex Network Analysis of Photovoltaic Plant Operations and Failure Modes," Energies, MDPI, vol. 12(10), pages 1-14, May.
    3. Tripathy, M. & Sadhu, P.K. & Panda, S.K., 2016. "A critical review on building integrated photovoltaic products and their applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 451-465.
    4. Walker, Linus & Hofer, Johannes & Schlueter, Arno, 2019. "High-resolution, parametric BIPV and electrical systems modeling and design," Applied Energy, Elsevier, vol. 238(C), pages 164-179.
    5. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    6. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    7. Dong Ji & Cai Zhang & Mingsong Lv & Ye Ma & Nan Guan, 2017. "Photovoltaic Array Fault Detection by Automatic Reconfiguration," Energies, MDPI, vol. 10(5), pages 1-13, May.
    8. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
    9. Sohel, M. Imroz & Ma, Zhenjun & Cooper, Paul & Adams, Jamie & Scott, Robert, 2014. "A dynamic model for air-based photovoltaic thermal systems working under real operating conditions," Applied Energy, Elsevier, vol. 132(C), pages 216-225.
    10. Pereira, Marcio Giannini & Camacho, Cristiane Farias & Freitas, Marcos Aurélio Vasconcelos & Silva, Neilton Fidelis da, 2012. "The renewable energy market in Brazil: Current status and potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3786-3802.
    11. 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.
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