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Photovoltaic Panel Faults Diagnosis: Based on the Fill Factor Analysis and Use of Artificial Intelligence Techniques

  • Research Article-Electrical Engineering
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Abstract

Solar energy has become a clean renewable source of electricity significantly demanded, after the marked improvements in the efficiency of solar panels due to the development of semiconductor materials science around the world. The performance of a solar panel is not restricted in terms of design and materials, but it is greatly affected by faults that can disturb or at least minimize their performances. In order to face these dysfunctions and identify them as soon as they appear, some techniques and methods have been proposed. They are classified according to the literature into statistical methods, analytical methods, artificial intelligence methods. A literature review of recent diagnosis methods, allowed us to propose in this work a diagnosis method based on the use of the fill factor FF and the maximum value of the short-circuit current Isc as inputs parameters, this method in addition to its simplicity, was proved its reliability and efficiency. In addition to the short-circuit current chosen by the majority of works as input data in the diagnosis and the detection of faults, this paper propose the use of a new criterion which is the fill factor in order to refine and make the diagnosis of the various faults profitable. The choice of these two criteria is justified by their importance: the variation of the short-circuit current is a significant and variable value according to the state of the photovoltaic cell, while the fill factor visualize more the efficiency and the resulted characteristic current–voltage. The diagnosis will proceed through simulation under MATLAB environment, in two steps: the first step of diagnosis based on threshold detection in which the identification of defects is done only by considering the threshold of each symptom, while the second step of diagnosis is based on artificial intelligence techniques in particular for cases with the same fault symptoms. At the end of this work, a simplified fault diagnostic method can be proposed, based on the use of the fill factor and the maximum value of the short-circuit current using artificial intelligence techniques. This methodology permit us to diagnose efficiently the presence of faults on photovoltaic panels.

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Authors and Affiliations

  1. Electrical Engeneering Laboratory LGEB, University of Biskra, Biskra, Algeria

    Abdelhamid Bouzaher & Amel Terki

  2. Scientific and Technical Research Centre for Arid Areas (CRSTRA), 07000, Biskra, Algeria

    Mohamed Tahar Bouzaher

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  1. Abdelhamid Bouzaher
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  2. Amel Terki
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  3. Mohamed Tahar Bouzaher
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Correspondence to Abdelhamid Bouzaher.

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Bouzaher, A., Terki, A. & Bouzaher, M.T. Photovoltaic Panel Faults Diagnosis: Based on the Fill Factor Analysis and Use of Artificial Intelligence Techniques. Arab J Sci Eng 48, 6471–6487 (2023). https://doi.org/10.1007/s13369-022-07409-w

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