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A Method for Estimating On-Field Photovoltaics System Efficiency Using Thermal Imaging and Weather Instrument Data and an Unmanned Aerial Vehicle

Author

Listed:
  • Wei-Hsiang Chiang

    (College of Photonics, National Yang Ming Chiao Tung University, Tainan 71150, Taiwan)

  • Han-Sheng Wu

    (College of Photonics, National Yang Ming Chiao Tung University, Tainan 71150, Taiwan)

  • Jong-Shinn Wu

    (Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan)

  • Shiow-Jyu Lin

    (Department of Electronic Engineering, National Ilan University, Ilan 260007, Taiwan)

Abstract

A new approach is proposed for estimating the power efficiency of an on-field solar photovoltaics (PV) system using data from thermal imaging and weather instruments obtained using an unmanned aerial vehicle (UAV). This method is specifically designed for the non-intrusive detection of the performance of the PV system in a large-scale solar power plant that could be efficient, manpower saving, operationally safe and comprehensive. In this study, a drone instrumented with a radiometer, a thermometer and an anemometer flew at a height of 1.5 m with a maximum lateral flight speed of 3.6 m/s above the PV modules (60 cells each) with hotspots or with aging but without hotspots. The average temperatures of the PV modules were then calculated through the measured radiation intensity, ambient temperature and wind speed based on the published correlation formula. The experimental correlations were obtained by measuring over 60 aging PV modules without hot-spot damage, and the uncertainties of the estimated efficiencies fell between 2% and 5%. Through the use of 20 hot-spot damaged PV modules when the measured temperatures of the cells were in the range of 80–90 °C, it was found that based on the experimental correlationd, their power efficiencies would be lower than 40% if more than eight cells had hot spots in a PV module. By taking this simple measure, the operator can decide which PV module is damaged and should be replaced immediately. By taking such measures, one can reduce the loading effect of solar PV modules adjacent to them because of the low efficiency and high impedance caused by the damage. We believe the new approach developed in this study could be very cost-effective and time-saving for improving the efficiency of power plant operations.

Suggested Citation

  • Wei-Hsiang Chiang & Han-Sheng Wu & Jong-Shinn Wu & Shiow-Jyu Lin, 2022. "A Method for Estimating On-Field Photovoltaics System Efficiency Using Thermal Imaging and Weather Instrument Data and an Unmanned Aerial Vehicle," Energies, MDPI, vol. 15(16), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5835-:d:885845
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    References listed on IDEAS

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    1. Fu, Huide & Li, Guiqiang & Li, Fubing, 2019. "Performance comparison of photovoltaic/thermal solar water heating systems with direct-coupled photovoltaic pump, traditional pump and natural circulation," Renewable Energy, Elsevier, vol. 136(C), pages 463-472.
    2. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    3. Khordehgah, Navid & Guichet, Valentin & Lester, Stephen P. & Jouhara, Hussam, 2019. "Computational study and experimental validation of a solar photovoltaics and thermal technology," Renewable Energy, Elsevier, vol. 143(C), pages 1348-1356.
    4. Fine, Jamie P. & Dworkin, Seth B. & Friedman, Jacob, 2019. "A methodology for predicting hybrid solar panel performance in different operating modes," Renewable Energy, Elsevier, vol. 130(C), pages 1198-1206.
    5. Dong Ho Lee & Jong Hwa Park, 2019. "Developing Inspection Methodology of Solar Energy Plants by Thermal Infrared Sensor on Board Unmanned Aerial Vehicles," Energies, MDPI, vol. 12(15), pages 1-14, July.
    6. Yadav, Kamlesh & Kumar, Atul & Sastry, O.S. & Wandhare, Rupesh, 2019. "Solar photovoltaics pumps operating head selection for the optimum efficiency," Renewable Energy, Elsevier, vol. 134(C), pages 169-177.
    7. Oswaldo Menéndez & Robert Guamán & Marcelo Pérez & Fernando Auat Cheein, 2018. "Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization," Energies, MDPI, vol. 11(7), pages 1-23, June.
    8. Hasan, M. Arif & Sumathy, K., 2010. "Photovoltaic thermal module concepts and their performance analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1845-1859, September.
    9. Ba, Moustapha & Ramenah, Harry & Tanougast, Camel, 2018. "Forseeing energy photovoltaic output determination by a statistical model using real module temperature in the north east of France," Renewable Energy, Elsevier, vol. 119(C), pages 935-948.
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