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Detection, location, and diagnosis of different faults in large solar PV system—a review

Author

Listed:
  • G R Venkatakrishnan
  • R Rengaraj
  • S Tamilselvi
  • J Harshini
  • Ansheela Sahoo
  • C Ahamed Saleel
  • Mohamed Abbas
  • Erdem Cuce
  • C Jazlyn
  • Saboor Shaik
  • Pinar Mert Cuce
  • Saffa Riffat

Abstract

Over the past decade, the significance of solar photovoltaic (PV) system has played a major role due to the rapid growth in the solar PV industry. Reliability, efficiency and safety of solar PV systems can be enhanced by continuous monitoring of the system and detecting the faults if any as early as possible. Reduced real time power generation and reduced life span of the solar PV system are the results if the fault in solar PV system is found undetected. Therefore, it is mandatory to identify and locate the type of fault occurring in a solar PV system. The faults occurring in the solar PV system are classified as follows: physical, environmental, and electrical faults that are further classified into different types as described in this paper. Once a fault is located and detected, an appropriate diagnosis method needs to be used to rectify it. In this paper, a comprehensive review of diverse fault diagnosis techniques reported in various literature is listed and described. This paper helps the researchers to get an awareness of the various faults occurring in a solar PV system and enables them to choose a suitable diagnosis technique based on its performance metrics to rectify the fault occurring in solar PV systems.

Suggested Citation

  • G R Venkatakrishnan & R Rengaraj & S Tamilselvi & J Harshini & Ansheela Sahoo & C Ahamed Saleel & Mohamed Abbas & Erdem Cuce & C Jazlyn & Saboor Shaik & Pinar Mert Cuce & Saffa Riffat, 2023. "Detection, location, and diagnosis of different faults in large solar PV system—a review," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 659-674.
    • Handle: RePEc:oup:ijlctc:v:18:y:2023:i::p:659-674.
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    References listed on IDEAS

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    1. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
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    4. Erdem Cuce & Saffa B. Riffat, 2017. "A smart building material for low/zero carbon applications: heat insulation solar glass—characteristic results from laboratory and in situ tests," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 12(2), pages 126-135.
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