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An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

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  • Selma Tchoketch Kebir

    (Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
    Unité de Développement des Equipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou-Ismail, Tipaza 42415, Algeria
    Laboratoire de Dispositifs de Communications et de Conversion Photovoltaique, Ecole Nationale Polytechnique, 10 Avenue Hassen Badi BP 182 El-Harrarach, Algiers 16200, Algeria)

  • Nawal Cheggaga

    (Laboratory of Electrical Systems and Remote Control, University Saad Dahleb of Blida1, P.O. Box 270 Route de Soumaa, Blida 0900, Algeria)

  • Adrian Ilinca

    (Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada)

  • Sabri Boulouma

    (Unité de Développement des Equipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou-Ismail, Tipaza 42415, Algeria)

Abstract

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

Suggested Citation

  • Selma Tchoketch Kebir & Nawal Cheggaga & Adrian Ilinca & Sabri Boulouma, 2021. "An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array," Sustainability, MDPI, vol. 13(11), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6194-:d:566322
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    References listed on IDEAS

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    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Yihua Hu & Wenping Cao, 2016. "Theoretical Analysis and Implementation of Photovoltaic Fault Diagnosis," Chapters, in: Wenping Cao & Yihua Hu (ed.), Renewable Energy - Utilisation and System Integration, IntechOpen.
    3. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
    4. 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.
    5. Pedro Branco & Francisco Gonçalves & Ana Cristina Costa, 2020. "Tailored Algorithms for Anomaly Detection in Photovoltaic Systems," Energies, MDPI, vol. 13(1), pages 1-21, January.
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    Cited by:

    1. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    2. Ana-Maria Moldovan & Mircea Ion Buzdugan, 2023. "Prediction of Faults Location and Type in Electrical Cables Using Artificial Neural Network," Sustainability, MDPI, vol. 15(7), pages 1-19, April.
    3. Adel Mellit & Omar Herrak & Catalina Rus Casas & Alessandro Massi Pavan, 2021. "A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays," Sustainability, MDPI, vol. 13(23), pages 1-14, November.

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