Browsing by Author "Boutora, Saliha (Supervisor)"

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Efficiency and performance evaluation of string inverters versus micro-Inverters in photovoltaic system
(Université M’Hamed Bougara de Boumerdes : Institut de génie electrique et electronique (IGEE), 2024) Kraled Kebir, Nadjib; Lairedj, Fayçal; Boutora, Saliha (Supervisor)
String inverters and micro-inverters represent distinct approaches to maximizing the power output from solar panels. This project is a comparative analysis of string inverters and micro-inverters within the context of photovoltaic (PV) systems. The primary objective is to evaluate the efficiency ,performance, and overall impact of these two inverter technologies on solar energy conversion. String inverters, which connect multiple solar panels in series, are analyzed for their cost-effectiveness and efficiency under uniform irradiance conditions. In contrast, micro-inverters, which are deployed at the module level, are evaluated for their ability to optimize energy harvest in scenarios with partial shading and varying irradiance. The study involves both theoretical modeling and simulations toquantify the performance metrics of each technology. Key parameters such as energy yield, system reliability, installation complexity, and maintenance requirements are assessed. Results from the analysis highlight the conditions under which each inverter type excels and provide insights into the decision-making process for PV system designers and installers.
Model predictive optimization based energy storage system in distributed system
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2023) Riche, Aboubaker; Boutora, Saliha (Supervisor)
The main goal of this project is to develop an MPC controller they will improve the performance of the storage system , by controlling the power flow between the battery energy storage system (BESS) and the supercapacitor (SC). To accomplish task stressing grid-forming hybrid energy storage systems and the supercapacitor's state of charge (SoC) by simulate rapid load variations and fast photovoltaic (PV) power fluctuations, also developing an MPC controller Is depends to efficient controlling power flow, restore to the SoC of SC after sudden load changes and limits its SoC variation in a predefined range, to ensure the continuous operation of SC. The performance of the proposed approach is then simulated using MATLAB/Simulink.
Power factor correction using neural network as controller for synchronous motor
(2023) Della, Mohammed Zakarya; M’sallaoui, Bilel; Boutora, Saliha (Supervisor)
This study presents a novel technique based on artificial neural networks (ANNs ) to correct the line power factor with variable loads by controlling the excitation current of a synchronous motor. The network is trained using voltage, current, and excitation current input-output pairs, which were collected using a test rig specifically designed for data collection. The desired outputs for training the neural network were assigned based on the experimental setup and requirements. The neural network was trained using the Bayesian regularization learning algorithm, which minimizes the error between the actual and desired output. The training process was conducted using the NN fitting tool in MATLAB, a widely-used software for developing and implementing neural networks.