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An Improved Fault Diagnosis in Stand-Alone Photovoltaic System Using Artificial Neural Network

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Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

This paper proposes an improved fault diagnosis for stand-alone photovoltaic (SAPV) system using artificial neural network (ANN) and power loss parameters as inputs. Unlike the classical power analysis approach that fails to find precisely the fault type, this method can accurately identify the fault class and can be used in real-time applications. The development of the ANN fault diagnosis model goes through data of both normal and faulty operation of the SAPV system. These data are obtained from either a real measurement to represent the normal operation where simplicity and safety are concerned or from the simulation in which the data of faults could hardly and costly be obtained. Three common types of ANN were trained, tested, and compared to choose the most efficient network to predict the faults in the system. The results indicate that multi-layer-perceptron network type is the most accurate network to recognize the faults with 95%. In addition, some test has been carried out in real-time to show their effectiveness.

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Abbreviations

ANN:

Artificial neural network

SAPV:

Stand-alone photovoltaic

MLP:

Multi-layer perceptron

PV:

Photovoltaic

IEA:

International energy agency

CDER:

Centre de developpement des energies renouvelable

PNN:

Probabilistic neural network

RBFN:

Radial basis function network

MSE:

Mean square error

LED:

Light emitting diode

I_V:

Current_voltage

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Authors and Affiliations

  1. Laboratory of Electrical Engineering and Automatic, LREA, University of Médéa, Médéa, Algeria

    Nassim Sabri & Abdelhalim Tlemçani

  2. Laboratory of Electrical Engineering, LGE, University of M’sila, M’sila, Algeria

    Aissa Chouder

  3. Centre de Développement des Energies Renouvelable, CDER, Bouzareah, Algeria

    Walid Merrouche

  4. Physics Departement of Materials, USTHB, Algiers, Algeria

    Walid Merrouche

Authors
  1. Nassim Sabri
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  2. Abdelhalim Tlemçani
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  3. Aissa Chouder
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  4. Walid Merrouche
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Correspondence to Abdelhalim Tlemçani.

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Sabri, N., Tlemçani, A., Chouder, A. et al. An Improved Fault Diagnosis in Stand-Alone Photovoltaic System Using Artificial Neural Network. Iran J Sci Technol Trans Electr Eng 48, 325–336 (2024). https://doi.org/10.1007/s40998-023-00671-0

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