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A field-applicable health monitoring method for photovoltaic system

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  • Han, Changwoon
  • Lee, Hyeonseok

Abstract

Solar cell modules are connected in series and parallel in a photovoltaic system. All the solar cell modules degrade over time and the most degraded module in a string decides the output level of the string. In this study, we suggest a health monitoring method which enables to detect the most degraded module in a string without separating the module from the string. The method places a non-transparent film on a module in a string to make an artificial shading effect and monitors the current-voltage curve of the string while placing the film to the next one. We show analytically that the most degraded module can be detected by comparing all the string current-voltage curves. We demonstrate the method on an outdoor photovoltaic string.

Suggested Citation

  • Han, Changwoon & Lee, Hyeonseok, 2019. "A field-applicable health monitoring method for photovoltaic system," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 219-227.
    • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:219-227
    DOI: 10.1016/j.ress.2018.01.002
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    References listed on IDEAS

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    1. Tsanakas, John A. & Ha, Long & Buerhop, Claudia, 2016. "Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 695-709.
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    Cited by:

    1. Yuan, Tao & Wu, Xinying & Bae, Suk Joo & Zhu, Xiaoyan, 2019. "Reliability assessment of a continuous-state fuel cell stack system with multiple degrading components," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 157-164.
    2. Yadav, Anurag Singh & Mukherjee, V., 2021. "Conventional and advanced PV array configurations to extract maximum power under partial shading conditions: A review," Renewable Energy, Elsevier, vol. 178(C), pages 977-1005.
    3. 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.
    4. Veljanovski, N. & ÄŒepin, M., 2024. "Event tree-based risk and financial assessment for power plants," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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