Fault detection in photovoltaic systems using the inverse of the belonging individual Gaussian probability
More details
Hide details
Department of electrical engineering, University of Mostaganem, Road Belahcel 27000 - Mostaganem Algeria
Unité de Recherche en Energie Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Energies Renouvelables, CDER, 01000, Adrar, Algeria
Submission date: 2022-11-06
Final revision date: 2023-01-06
Acceptance date: 2023-02-16
Online publication date: 2023-02-20
Publication date: 2023-02-20
Corresponding author
Salah Sendjasni   

Department of electrical engineering, University of Mostaganem, Road Belahcel 27000 - Mostaganem Algeria
Diagnostyka 2023;24(1):2023112
This article addresses the problem of fault early detection in photovoltaic systems. In the production field, solar power plants consist of many photovoltaic arrays, which may suffer from many different types of malfunctions over time. Hence, fault early detection before it affects PV systems and leads to a full system failure is essential to monitor these systems. The fields of control and monitoring of systems have been extensively approached by many researchers using various fault detection methods. Despite all this research, to early detect and locate faults in a very large photovoltaic power plant, we must, in particular, think of an effective method that allows us to do so at the lowest costs and time. Thus, we propose a new robust technique based on the inverse of the belonging individual Gaussian probability (IBIGP) to early detect and locate faults in the power curve as well as in the Infrared image of the photovoltaic systems. While most fault detection methods are well incorporated in other domains, the IBIGP technique is still in its infancy in the photovoltaic field. We will show, however, in this work that the IBIGP technique is a very promising tool for fault early detection enhancement.
Tansel C, Topcu M. Total, renewable and non-renewable energy consumption and economic growth : Revisiting the issue with an asymmetric point of view. Energy. 2018;152:64–74. https://doi.org/10.1016/j.ener....
Shahbaz M, Raghutla C, Chittedi KR, Jiao Z. The effect of renewable energy consumption on economic growth: Evidence from the renewable energy country attractive index. Energy. 2020; 207: 118162. https://doi.org/10.1016/j.ener....
Science E. Substitution of energy needs with renewable energy sources. Earth and Environmental Science. 2021; 6th International Energy Conference. 012032. https://doi.org/10.1088/1755-1....
Oberle B, Bringezu S, Hatfield-Dodds A, Zhu B. Global resources outlook 2019. natural resources for the future we want. 2019.
Tabrizian S. Technological innovation to achieve sustainable development — Renewable energy technologies diffusion in developing countries.Sustainable Development. 2019;30:537-544. https://doi.org/10.1002/sd.191....
Kannan N, Vakeesan D. Solar energy for future world: - A review. Renewable and Sustainable Energy Reviews. 2016; 62: 1092–1105. https://doi.org/10.1016/j.rser....
Hayat MB, Ali D, Cathrine K, Lana M, Ahmed N. Solar energy — A look into power generation , challenges , and a solar ‐ powered future. International Journal of Energy Research. 2019 ; 43 : 1049-1067. https://doi.org/10.1002/er.425....
Aouchiche N. Défauts liés aux systèmes photovoltaïques autonomes et techniques de diagnostic - Etat de l’art. Journal of Renewable Energies. 2018; 21 :247–265.
Pillai DS, Rajasekar N. A comprehensive review on protection challenges and fault diagnosis in PV systems. Renew. Sustain. Energy Rev. 2018;91:18–40. https://doi.org/10.1016/j.rser....
Omran AH, Ahmad N. A Survey of Different DC Faults in a Solar Power System. IEEE 8th Conference on Systems. Process and Control. 2020:11–12. https://doi.org/10.1109/ICSPC5....
Triki-Lahiani A, Bennani-Ben Abdelghani A, Slama-Belkhodja I. Fault detection and monitoring systems for photovoltaic installations: A review. Renew. Sustain. Energy Rev.2018; 82:2680–2692. https://doi.org/10.1016/j.rser....
Appiah AY, Zhang X, Beklisi B, Ayawli K, Kyeremeh F. Review article review and performance evaluation of photovoltaic array fault detection and diagnosis techniques. 2019. https://doi.org/10.1155/2019/6....
Ali K, Niazi K, Akhtar W, Khan HA, Yang Y, Athar S. Hotspot diagnosis for solar photovoltaic modules using a Naive Bayes classifier. Sol. Energy. 2019; 190: 34–43. https://doi.org/10.1016/j.sole....
Colaprico M, De Ruvo MF, Leotta G, Vergura S, Marino F. DUBIO: a fully automatic Drones & cloUd Based Infrared monitoring system for large-scale PV plants. 2018 IEEE Int. Conf. Environ. Electr. Eng. IEEE Ind. Commer. Power Syst. Eur. (EEEIC/ I&CPS Eur. 2019. https://doi.org/10.1109/EEEIC.....
Ziane A, et al. Detecting partial shading in grid-connected PV station using random forest classifier. Artificial Intelligence and Renewables Towards an Energy Transition. 2021;174:88–95. https://doi.org/10.1007/978-3-....
Abderrezzaq Z, et al. Performance analysis of a grid connected photovoltaic station in the region of Adrar. 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B), 2017: 1–6. https://doi.org/10.1109/ICEE-B....
Necaibia A, et al. Analytical assessment of the outdoor performance and efficiency of grid-tied photovoltaic system under hot dry climate in the south of Algeria. Energy Convers. Manag.2018;171: 778–786. https://doi.org/10.1016/j.enco....
Yagoubi B. A geometric approach to a non-stationary process. In: Proceedings of the 2nd international conference on Mathematical Models for Engineering Science, and proceedings of the 2nd international conference on Development. 2011; 2: 179–183.
Hida Takeyuki, Masuyuki Hitsuda. Gaussian processes. American Mathematical Soc., 1993;120.
Rice SO. Mathematical analysis of random noise. The Bell System Technical Journal. 1944;23(3):282-332.
Chan SC, Zhou Y. On the performance analysis of a class of transform-domain NLMS algorithms with Gaussian inputs and mixture Gaussian additive noise environment. J. Signal Process. Syst. 2011; 64: 429–445. https://doi.org/10.1007/s11265....