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Using EMPHASIS for the Thermography-Based Fault Detection in Photovoltaic Plants

Author

Listed:
  • Antonio Pio Catalano

    (Department of Electrical Engineering and Information Technology, University ‘Federico II’, 80125 Naples, Italy)

  • Ciro Scognamillo

    (Department of Electrical Engineering and Information Technology, University ‘Federico II’, 80125 Naples, Italy)

  • Pierluigi Guerriero

    (Department of Electrical Engineering and Information Technology, University ‘Federico II’, 80125 Naples, Italy)

  • Santolo Daliento

    (Department of Electrical Engineering and Information Technology, University ‘Federico II’, 80125 Naples, Italy)

  • Vincenzo d’Alessandro

    (Department of Electrical Engineering and Information Technology, University ‘Federico II’, 80125 Naples, Italy)

Abstract

In this paper, an Efficient Method for PHotovoltaic Arrays Study through Infrared Scanning (EMPHASIS) is presented; it is a fast, simple, and trustworthy cell-level diagnosis method for commercial photovoltaic (PV) panels. EMPHASIS processes temperature maps experimentally obtained through IR cameras and is based on a power balance equation. Along with the identification of malfunction events, EMPHASIS offers an innovative feature, i.e., it estimates the electrical powers generated (or dissipated) by the individual cells. A procedure to evaluate the accuracy of the EMPHASIS predictions is proposed, which relies on detailed three-dimensional (3-D) numerical simulations to emulate realistic temperature maps of PV panels under any working condition. Malfunctioning panels were replicated in the numerical environment and the corresponding temperature maps were fed to EMPHASIS. Excellent results were achieved in both the cell- and panel-level power predictions. More specifically, the estimation of the power production of a PV panel with a shunted cell demonstrated an error lower than 1%. In cases of strong nonuniformities as a PV panel in hotspot, an estimation error in the range of 9–16% was quantified.

Suggested Citation

  • Antonio Pio Catalano & Ciro Scognamillo & Pierluigi Guerriero & Santolo Daliento & Vincenzo d’Alessandro, 2021. "Using EMPHASIS for the Thermography-Based Fault Detection in Photovoltaic Plants," Energies, MDPI, vol. 14(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1559-:d:515208
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    References listed on IDEAS

    as
    1. 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.
    2. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
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