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New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems

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Listed:
  • Kara Mostefa Khelil, Chérifa
  • Amrouche, Badia
  • Benyoucef, Abou soufiane
  • Kara, Kamel
  • Chouder, Aissa

Abstract

The present work brings a new intelligent algorithm for PV system’s diagnosis and fault detection (IFD). At this stage of the study, this algorithm can detect and identify three recurrent cases between healthy and short circuit faults, as well as string disconnection in PV array using artificial neural networks (ANN). Both, detection and isolation are simple and fast. The developed model requires small training period and is based on only four inputs: the maximum power current and voltage from the output current-voltage (I–V) characteristic, the solar irradiation and the cell temperature. Experimental validation of the proposed IFD has been carried on small grid connected PV generator (PVG). The obtained results demonstrate that this approach can precisely detect and classify the existing faults with high accuracy (98.6%).

Suggested Citation

  • Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220316996
    DOI: 10.1016/j.energy.2020.118591
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    References listed on IDEAS

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    1. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    2. 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.
    3. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
    4. 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.
    5. Amrouche, Badia & Guessoum, Abderrezak & Belhamel, Maiouf, 2012. "A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison," Applied Energy, Elsevier, vol. 91(1), pages 395-404.
    6. Bastidas-Rodriguez, J.D. & Franco, E. & Petrone, G. & Ramos-Paja, C.A. & Spagnuolo, G., 2017. "Quantification of photovoltaic module degradation using model based indicators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 101-113.
    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. Hare, James & Shi, Xiaofang & Gupta, Shalabh & Bazzi, Ali, 2016. "Fault diagnostics in smart micro-grids: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1114-1124.
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    Cited by:

    1. Kapucu, Ceyhun & Cubukcu, Mete, 2021. "A supervised ensemble learning method for fault diagnosis in photovoltaic strings," Energy, Elsevier, vol. 227(C).
    2. Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
    3. Lin, Meng & Li, Jiangkuan & Li, Yankai & Wang, Xu & Jin, Chengyi & Chen, Junjie, 2023. "Generalization analysis and improvement of CNN-based nuclear power plant fault diagnosis model under varying power levels," Energy, Elsevier, vol. 282(C).
    4. Ali, Aliyuda, 2021. "Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns," Energy, Elsevier, vol. 229(C).

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