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Characterization of the insulation and leakage currents of PV generators: Relevance for human safety

Author

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  • Hernández, J.C.
  • Vidal, P.G.
  • Medina, A.

Abstract

Protection against electric shock in photovoltaic generators (PVGs) with active protective measures requires an in-depth knowledge of the electrical behaviour of PVG insulation and PVG response under operating conditions with insulation faults. On the one hand, this knowledge can be obtained with an equivalent circuit model that characterises this insulation. The model presented can be used to: (i) evaluate PVG insulation resistance and leakage current; (ii) analyse potential hazards for the general public; (iii) design the best means of protection. On the other hand, this article also describes the insulation of a functioning PVG, and its reaction to meteorological variables (MVs) in laboratory and field conditions. Test results highlight those MVs that have a greater influence on PVG insulation as well as the relation between weather and insulation. This type of characterization is crucial when it comes to testing the operating capacity of the protective devices used in active protective measures against electric shock under different meteorological conditions.

Suggested Citation

  • Hernández, J.C. & Vidal, P.G. & Medina, A., 2010. "Characterization of the insulation and leakage currents of PV generators: Relevance for human safety," Renewable Energy, Elsevier, vol. 35(3), pages 593-601.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:3:p:593-601
    DOI: 10.1016/j.renene.2009.08.006
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    Cited by:

    1. Muñoz, J.V. & Nofuentes, G. & Aguilera, J. & Fuentes, M. & Vidal, P.G., 2011. "Procedure to carry out quality checks in photovoltaic grid-connected systems: Six cases of study," Applied Energy, Elsevier, vol. 88(8), pages 2863-2870, August.
    2. Venkata Anand Prabhala & Bhanu Prashant Baddipadiga & Poria Fajri & Mehdi Ferdowsi, 2018. "An Overview of Direct Current Distribution System Architectures & Benefits," Energies, MDPI, vol. 11(9), pages 1-20, September.
    3. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    4. 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.
    5. Silvestre, Santiago & Kichou, Sofiane & Chouder, Aissa & Nofuentes, Gustavo & Karatepe, Engin, 2015. "Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions," Energy, Elsevier, vol. 86(C), pages 42-50.
    6. Rajput, Pramod & Shyam, & Tomar, Vivek & Tiwari, G.N. & Sastry, O.S. & Bhatti, T.S., 2018. "A thermal model for N series connected glass/cell/polymer sheet and glass/cell/glass crystalline silicon photovoltaic modules with hot solar cells connected in series and its thermal losses in real ou," Renewable Energy, Elsevier, vol. 126(C), pages 370-386.

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