Publications Scientifiques

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Author: search.filters.author.Bouchetob, Elaid

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    Deep learning and wbg devices combining to improve pv system efficiency: anfis-based mppt controller
    (2025) Bouchetob, Elaid; Nadji, Bouchra
    With the escalating demand for renewable energy sources, photovoltaic (PV) systems have emerged as a pivotal solution for sustainable power generation. The efficacy of these systems is paramount for their widespread implementation. This research article delves into the efficiency assessment of silicon carbide (SiC) components within a boost converter integrated into a PV system. Notably, the boost converter switch is under the intelligent control of an adaptive neuro-fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) controller. This innovative approach leverages AI to optimize energy extraction from PV panels, thereby enhancing overall system efficiency. The cooperation of SiC components and AI-driven control presents a novel perspective on robust and efficient PV systems. To substantiate the research, data collected from the Sidi Bel-Abès PV central is utilized to train the ANFIS. The utilization of real-world data enhances the accuracy of the predictive model, thereby increasing its applicability to practical scenarios. Integrating AI technologies with PV systems marks a significant advancement toward intelligent and adaptive energy systems
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    Boosting Reliability: A Comparative Study of Silicon Carbide (Sic) and Silicon (Si) in Boost Converter Design Using MIL-HDBK-217
    (J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology, 2024) Bouchetob, Elaid; Nadji, Bouchra
    Reliability is very important in the world of electronic device design and production, particularly in applications where continuous and flawless performance is a necessity. This directs our attention to the boost converter, which forms the foundation of power electronics, renewable energy systems, and electric vehicles. However, as technology progresses, the choice of materials for these converters is a big challenge. For that, in this paper, the impact of using Silicon Carbide (SiC) devices, with their promising material properties, on the reliability of boost converters is presented. Because the results showed that more than 80% of boost converter failures are caused by semiconductors, the use of SiC materials is assessed by determining its reliability using MIL-HDBK-217 standard. In addition, a comparative study with the use of traditional Silicon (Si) is conducted. The results showed that the failure rate of boost converters based on SiC devices reduced from 8.335 failure/10-6h to 6.243 failure/10-6h. This notable shift in failure rates establishes SiC as a pivotal material in the evolution of boost converter technology, offering a compelling solution to address the persistent challenges associated with semiconductor-related failures.
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    Efficiency comparison of silicon and silicon carbide MOSFETs in a PV system application
    (IEEE, 2023) Bouchetob, Elaid; Nadji, Bouchra; Mahdi, Ismahan
    This research includes a comparative assessment of the efficiency of DC-DC converters in a PV system that are based on silicon and silicon carbide Mosfets. The inquiry compares the two types of MOSFETs. The maximum power point tracking (MPPT) method is used to control the DC-DC converter, which receives electricity from a solar array with a 1 kW capacity. Along with being employed for the MPPT genetic algorithms, Matlab Simulink was used throughout the entire development of the solar array. After completing this stage, the next step involves using Ansys Simplorer to do a simulation of the MOSFETs. The last phase, which consisted of creating a Co-Simulation between Matlab/Simulink and Ansys Simplorer, has now been completed. In order to improve the efficiency of the system as a whole, we are able to switch out the Si Mosfets that are now being used in the PV application for SiC Mosfets. This is feasible because of the superior performance of SiC MOSFETs in terms of both response speed and the amount of energy lost in the process
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    Choosing the adapted artificial intelligence method (ANN and ANFIS) based MPPT controller for thin layer PV array
    (Springer, 2023) Bouchetob, Elaid; Nadji, Bouchra
    Because of the many advantages that artificial intelligence technologies provide in comparison to more conventional methods, a rising number of solar power plants are beginning to use them in their monitoring of the MPP. When there is a sudden change in solar temperature and irradiance, it is possible that the MPP will not be tracked as accurately. As a consequence of this, these methods could make up for the deficiencies of those that are more well-established (P&O, IC, etc.). Aside from that, there is a wide range of methods to AI, each of which has a particular advantage. By making some minor adjustments to the architecture, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used to monitor the MPP of Thin Layer panel technology at the Oued Nechou installation in Ghardaia. Each connection channel now has six panels rather than the previous maximum of 12 panels, and the junction box has 210 channels rather than the prior maximum of 105 channels. In the last step, a DC-DC boost converter is used to increase the power output voltages produced by the module