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Novel deterioration diagnosis device for individual photovoltaic modules useable without disconnecting electric wiring in solar cell string

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  • Tanaka, Tadashi
  • Kuramochi, Taito
  • Ogawa, Hiroshi
  • Inui, Yoshitaka

Abstract

A novel deterioration diagnosis device for individual photovoltaic modules useable without disconnecting electric wiring in a solar cell string was proposed by improving the authors' previous deterioration diagnosis device for a single separated photovoltaic module. As with the previous device, this proposed device employed a xenon flash lighting system and a detector capacitor. An auxiliary diode network was newly installed as the additional improvement to enable connection of the previous device to the solar cell string and to realize the diagnosis of each photovoltaic module connected in the solar cell string. Before performing demonstration test experiments of the proposed device, preliminary assessment of its diagnostic capability was carried out through numerical simulations. The simulation results suggested the validity of the proposed device. Based on this positive evaluation, a prototype experimental apparatus of the proposed device was then assembled and the demonstration test experiments were carried out by using it. As a result, both the usability without disconnecting electric wiring in the solar cell string and sufficient deterioration diagnosis capability of the proposed deterioration diagnosis device for individual PV modules connected in a solar cell string were successfully confirmed.

Suggested Citation

  • Tanaka, Tadashi & Kuramochi, Taito & Ogawa, Hiroshi & Inui, Yoshitaka, 2024. "Novel deterioration diagnosis device for individual photovoltaic modules useable without disconnecting electric wiring in solar cell string," Renewable Energy, Elsevier, vol. 232(C).
  • Handle: RePEc:eee:renene:v:232:y:2024:i:c:s0960148124011790
    DOI: 10.1016/j.renene.2024.121111
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    References listed on IDEAS

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    1. Kamei, Aika & Yoshida, Shota & Takakura, Hideyuki & Minemoto, Takashi, 2014. "Ten years outdoor operation of silicon based photovoltaic modules at central latitude of Japan," Renewable Energy, Elsevier, vol. 65(C), pages 78-82.
    2. Triki-Lahiani, Asma & Bennani-Ben Abdelghani, Afef & Slama-Belkhodja, Ilhem, 2018. "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2680-2692.
    3. Chine, W. & Mellit, A. & Pavan, A. Massi & Kalogirou, S.A., 2014. "Fault detection method for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 66(C), pages 99-110.
    4. Wang, Mengyuan & Xu, Xiaoyuan & Yan, Zheng, 2023. "Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression," Renewable Energy, Elsevier, vol. 203(C), pages 68-80.
    5. Hussain, Muhammed & Dhimish, Mahmoud & Titarenko, Sofya & Mather, Peter, 2020. "Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters," Renewable Energy, Elsevier, vol. 155(C), pages 1272-1292.
    6. Lu, Shibo & Phung, B.T. & Zhang, Daming, 2018. "A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 88-98.
    7. 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.
    8. Bressan, M. & El Basri, Y. & Galeano, A.G. & Alonso, C., 2016. "A shadow fault detection method based on the standard error analysis of I-V curves," Renewable Energy, Elsevier, vol. 99(C), pages 1181-1190.
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