Power

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    Design of EV charging station based on a multisource system
    (Université M'hamed Bougara Boumerdès: Institue de génie electronic et electric, 2024) Teber, Amine; Ticherafi, Fouad; Ammar, Abdelkarim (supervisor)
    This report aims to create a battery charging system for electric vehicles (EVs) that incorporates several energy sources, such as a photovoltaic (PV) array, a backup battery, and the electrical grid. The objective is to create a charging station that can reliably produce a steady power output under a range of supply and environmental circumstances. The research begins with applying Maximum Power Point Tracking (MPPT), a technique essential for maximizing the energy harnessed from solar panels. This is followed by an exploration of Direct Power Control (DPC) for AC to DC conversion, which is crucial for maintaining power quality and stability. Additionally, the study delves into the cascade control of EV and station batteries to ensure seamless energy management across the system for differen tscenario sdependin go nth epowe rstate of the sources, and all implementations have been done using Matlab Simulink.
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    Maximum power point tracking for solar water pumping system under partial shading conditions
    (2024) Bouafia, Imad; Azioune, Ahmed; Ammar, Abdelkarim (supervisor)
    This report presents innovative approaches to maximize the power output of photovoltaic (PV) systems for water-pumping applications based on BLDC motor, specificall yaddress- ing challenges posed by partial shading, which occurs when certain parts of the PV array are shaded while others are exposed to sunlight. Traditional Maximum Power Point Tracking (MPPT) algorithms, such as the Perturb and Observe (P&O) method, have limitations when it comes to dealing with partial shading, as these algorithms struggle to accurately identify the global maximum power point (GMPP), which is important for achieving optimal power generation. To overcome these limitations, this work introduces advanced metaheuristic algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the Marine Predator Algorithm (MPA), to robustly track the GMPP, thus ensuring optimal performance despite variations in solar exposure. Furthermore, the efficienc yo feac halgorith mis compared using simulation models generated in MATLAB/Simulink, where the results demonstrate that these algorithms significantly improve power extraction under partial shading conditions.