Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks

  • Aicha Amani Djalab
    Affiliation

    Department of Electrical Engineering, Faculty of Technology, University of Djelfa, P. O. B. 3117, Djelfa, Algeria

  • Mohamed Mounir Rezaoui
    Affiliation

    Department of Electrical Engineering, Faculty of Technology, University of Djelfa, P. O. B. 3117, Djelfa, Algeria

  • Lakhdar Mazouz
    Affiliation

    Department of Electrical Engineering, Faculty of Technology, University of Djelfa, P. O. B. 3117, Djelfa, Algeria

  • Ali Teta
    Affiliation

    Department of Electrical Engineering, Faculty of Technology, University of Djelfa, P. O. B. 3117, Djelfa, Algeria

  • Nassim Sabri
    Affiliation

    Department of Electrical Engineering, Faculty of Technology, University of Medea, 26000 Medea, Algeria

Abstract

During their operation, PV systems can be subject of various faults and anomalies that could lead to a reduction in the effectiveness and the profitability of the PV systems. These faults can crash, cause a fire or stop the whole system. The main objective of this work is to present a sophisticated method based on artificial neural networks ANN for diagnosing; detecting and precisely classifying the fault in the solar panels in order to avoid a fall in the production and performance of the photovoltaic system. The work established in this paper intends in first place to propose a method to detect possible various faults in PV module using the Multilayer Perceptron (MLP) ANN network. The developed artificial neural network requires a large database and periodic training to evaluate the output parameters with good accuracy. To evaluate the accuracy and the performance of the proposed approach, a comparison is carried out with the classic method (the method of thresholding). To test the effectiveness of the proposed approach in detecting and classifying different faults, an extensive simulation is carried out using Matlab SIMULINK.

Keywords: photovoltaic system, diagnosis, faults detection, method of thresholding, artificial neural networks
Published online
2020-03-12
How to Cite
Djalab, A. A., Rezaoui, M. M., Mazouz, L., Teta, A., Sabri, N. “Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks”, Periodica Polytechnica Electrical Engineering and Computer Science, 64(3), pp. 291-302, 2020. https://doi.org/10.3311/PPee.14828
Section
Articles