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Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators

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  • Hocine, Labar
  • Samira, Kelaiaia Mounia
  • Tarek, Mesbah
  • Salah, Necaibia
  • Samia, Kelaiaia

Abstract

PV modules are costly devices, so, their lifetime is an important parameter in the investment evaluation. The aim of this paper is to propose an earlier degradation detection that affects glass, EVA, wires etc … Where many researchers propose degradation evaluation based on scheduled eye observation, which becomes problematic for large scale PV power production, because it takes much more time and mobilizes skilled workers. This type of degradation evaluation is very expensive and must be carried out by expert workers. To automate PV panels self evaluation, the degradations models are embedded in a microcontroller as software which operates with instantaneous measured parameters. The degradation phenomena of each PV module’s element are also presented and discussed. For this purpose an Observing Degradation System (ODS) program is proposed and detailed, based on modeling of each recognized degradation. Where new parameters are introduced to improve the fault type detection. This recognition method of degradation types based on P–V characteristics and checklist is developed and successfully tested.

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  • Hocine, Labar & Samira, Kelaiaia Mounia & Tarek, Mesbah & Salah, Necaibia & Samia, Kelaiaia, 2021. "Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators," Renewable Energy, Elsevier, vol. 164(C), pages 603-617.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:603-617
    DOI: 10.1016/j.renene.2020.09.094
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    References listed on IDEAS

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    Cited by:

    1. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    2. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
    3. Li, Baojie & Hansen, Clifford W. & Chen, Xin & Diallo, Demba & Migan-Dubois, Anne & Delpha, Claude & Jain, Anubhav, 2024. "A robust I–V curve correction procedure for degraded photovoltaic modules," Renewable Energy, Elsevier, vol. 224(C).
    4. Martín Antonio Rodríguez Licea & Francisco Javier Pérez Pinal & Allan Giovanni Soriano Sánchez, 2021. "An Overview on Electric-Stress Degradation Empirical Models for Electrochemical Devices in Smart Grids," Energies, MDPI, vol. 14(8), pages 1-23, April.

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