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Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems

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  • Fezai, R.
  • Mansouri, M.
  • Trabelsi, M.
  • Hajji, M.
  • Nounou, H.
  • Nounou, M.

Abstract

This paper proposes an effective kernel generalized likelihood ratio test (KGLRT) technique for fault detection in Photovoltaic (PV) systems. The proposed technique is considered as an improvement of the conventional KGLRT with extended online capabilities and lower computational complexity. The proposed online reduced KGLRT (OR-KGLRT) is based on transforming the process data into a higher dimensional space (where the data becomes linear), which makes the kernel-based scheme attractive for modeling nonlinear systems. The performance of the proposed method is evaluated and compared to the conventional KGLRT statistic using a simulated PV data. Both techniques are applied to detect single and multiple failures (including Bypass, Mismatch, Mix and Shading failures). The selected performance criteria are the good detection rate (GDR), false alarm rate (FAR), and computation time (CT). Simulation results show superior detection efficiency of the proposed approach compared to the conventional KGLRT statistic in terms of GDR, FAR and CT.

Suggested Citation

  • Fezai, R. & Mansouri, M. & Trabelsi, M. & Hajji, M. & Nounou, H. & Nounou, M., 2019. "Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems," Energy, Elsevier, vol. 179(C), pages 1133-1154.
    • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:1133-1154
    DOI: 10.1016/j.energy.2019.05.029
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    1. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    2. 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.
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    Cited by:

    1. Fezai, Radhia & Malluhi, Byanne & Basha, Nour & Ibrahim, Gasim & Choudhury, Hanif A. & Challiwala, Mohamed S. & Nounou, Hazem & Elbashir, Nimir & Nounou, Mohamed, 2023. "Bayesian optimization of multiscale kernel principal component analysis and its application to model Gas-to-liquid (GTL) process data," Energy, Elsevier, vol. 284(C).
    2. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    3. Bakdi, Azzeddine & Bounoua, Wahiba & Mekhilef, Saad & Halabi, Laith M., 2019. "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, Elsevier, vol. 189(C).

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