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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Mellit, A.; Tina, G.M.; Kalogirou, S.A.: Fault detection and diagnosis methods for photovoltaic systems: a review’. Renew. Sustain. Energy Rev. 91, 1–17 (2018)
Alam, M.K.; Khan, F.; Johnson, J.; Flicker, J.: A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques. IEEE J. Photovolt. 5(3), 982–997 (2015)
Belaout, A.; Krim, F.; Mellit, A.; Talbi, B.; Arabi, A.: Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification. Renew. Energy 127, 548–558 (2018)
Chine, W.; Mellit, A.; Lughi, V.; Malek, A.; Sulligoi, G.; Pavan, A.M.: A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renew. Energy 90, 501–512 (2016)
Deng, S.; Zhang, Z.; Ju, C.; Dong, J.; Xia, Z.; Yan, X.; Xu, T.; Xing, G.: Research on hot spot risk for high efficiency solar module. Energy Procedia 130, 77–86 (2017)
Vieira, R.G.; de Araújo, F.M.U.; Dhimish, M.; Guerra, M.I.S.: A comprehensive review on bypass diode application on photovoltaic modules. Energies 13(10), 2472 (2020)
Solheima, H.J.; Fjæra, H.G.; Sørheima, E.A.; Foss, S.E.: Measurement and simulation of hot spots in solar cells. Energy Procedia 38, 183–189 (2013)
Simon, M.; Meyer, E.L.: Detection and analysis of hot-spot formation in solar cells. Sol. Energy Mater Sol. Cells 94, 106–113 (2010)
Lorenzo, E.; Moretón, R.; Luque, I.: Dust effects on PV array performance: in-field observations with non-uniform patterns. Prog. Photovolt. Res. Appl. 22, 666–670 (2014)
Adinoyi, M.J.; Said, S.A.: Effect of dust accumulation on the power outputs of solar photovoltaic modules. Renew. Energy 60, 633–636 (2013)
Colli, A.: Failure mode and effect analysis for photovoltaic systems. Renew. Sustain. Energy Rev. 50, 804–809 (2015)
Tina, G.M.; Cosentino, F.; Ventura, C.: Monitoring and diagnostics of photovoltaic power plants. In: Sayigh, A. (Ed.) Renewable Energy in the Service of Mankind, Vol. II, pp. 505–516. Springer, Cham (2016)
Triki-Lahiani, A.; Abdelghani, A.B.-B.; Slama-Belkhodja, I.: Fault detection and monitoring systems for photovoltaic installations: a review. Renew. Sustain. Energy Rev. 82, 2680–2692 (2017)
Daliento, S.: Guerriero, P.; Pavan, A.M.: Mellit, A.: Moeini, R.: Tricoli, P.: Monitoring, diagnosis, and power forecasting for photovoltaic fields: a review . Int. J. Photoenergy (2017)
Wang, W.; Liu, A.C.F.; Chung, H.S.H.; Lau, R.W.H.; Zhang, J.; Lo, A.W.L.: Fault diagnosis of photovoltaic panels using dynamic current–voltage characteristics. IEEE Trans. Power Electron. 31, 1588–1599 (2016)
Belaout, A.: Magister thesis,‘’ Etude et diagnostic des défauts fréquents aux systèmes photovoltaïques (PV) par emploi de la caractéristique courant-tension’’, university SETIF 1, SETIF (2014)
Kumar, N. M.: Chopra, S. S.: de Oliveira, A. K. V.: Ahmed H.:Vaezi, S.: Madukanya, U. E.: Castanon, J. M.: Solar PV module technologies. Photovolt. Solar Energy Convers. (2020)
Khatib, T.; Elmenreich, W.: Modeling of photovoltaic systems using matlab. Wiley, London (2016)
Duflou, J.R.; Peeters, J.R.; Altamirano, D.; Bracquene, E.; Dewulf, W.: Demanufacturing photovoltaic panels: comparison of end-of-life treatment strategies for improved resource recovery. CIRP Annals Manuf. Technol. 67(1), 29–32 (2018)
Das, A.K.: ’An explicit J-V model of a solar cell for simple fill factor calculation’. Sol. Energy 85(9), 1906 (2011)
Köntges, M.: Kurtz, S.: Jahn, U.: Berger, K.: Kato, K.: Friesen, T.: et al. Review of failures of photovoltaic modules. IEA PVPS Task (2014).
Rezgui, W.: Mouss, H.: Mouss, N: Mouss, D.: Benbouzid, M.: Amirat, Y.: Photovoltaic module simultaneous open-and short-circuit faults modeling and detection using the I–V characteristic. In: Proceedings of the 24th IEEE International Symposium on Industrial Electronics (ISIE). p. 855–60. (2015)
Gupta, D.; Mukhopadhyay, S.; Narayan, K.S.: Fill factor in organic solar cells. Solar Energy Mater. Solar Cells 94, 1309–1313 (2010)
Qi, B.; Wang, J.: Fill factor in organic solar cells. Phys. Chem. Chem. Phys. 15(23), 8972–8982 (2013)
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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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DOI: https://doi.org/10.1007/s13369-022-07409-w