Browsing by Author "Chalah, Samira (Supervisor)"

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MPPT Algorithms for photovoltaic systems under partial shading conditions
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2023) Benrabah, Hamza; Chalah, Samira (Supervisor); Belmadani, Hamza
This master's thesis delves into the comprehensive study of Photovoltaic (PV) systems, focusing on various aspects critical to their efficient operation and optimization. The work is divided into five chapters, each addressing a distinct facet of PV technology and control techniques. Chapter 1: provides a foundational understanding of PV systems, including an introduction to solar energy, photovoltaic cell technology, PV modeling, and the characteristics of solar cell I-V curves. In Chapter 2: the focus shifts to DC-DC converters, exploring the principles and operation of Boost and Buck converters, as well as the versatile Buck-Boost inverter converter. The advantages of Boost converters are also highlighted. Chapter 3: delves into the critical topic of Maximum Power Point Tracking (MPPT), elucidating the principles behind MPPT operation, typical MPPT-based PV system configurations, and the classification of MPPT algorithms, including indirect and direct methods. Chapter 4: introduces soft computing algorithms and novel techniques for MPPT, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Seagull Optimization Algorithm (SOA), and the innovative Guided Seagull Optimizer. These algorithms play a pivotal role in enhancing the performance of PV systems. Finally, in Chapter 5, the thesis presents simulation and experimental results to validate the effectiveness of the discussed algorithms under varying irradiance conditions, offering insights into their real-world applicability and performance.This research contributes to the growing body of knowledge surrounding PV systems, offering valuable insights into their operation, optimization, and control, with a particular emphasis on the application of soft computing techniques to maximize energy extraction.
MPPT battery charge controller for PV hybrid systems
(2022) Boulaouche, Zaki; Abbas, Aimen; Chalah, Samira (Supervisor)
Interest in solar energy has grown significantly in recent years. Various topics are pursued to extend the performance of PV modules and the theoretical understanding of photovoltaics has been the subject of many researches. In hybrid solar energy systems, batteries are used as energy storage. Photovoltaic arrays provide energy during steady operating conditions, while batteries are used as energy sources during transient operating conditions (backup sources). This project report presents the design of a battery charging circuit that improves maximum power point (MPP) under variable solar irradiance and constant temperature at 25 °C through a smart MPPT (maximum power point tracking) algorithm. The boost converter output voltage drives an optimal PI-controlled Bi- directional DC/DC converter to behave like a battery charger circuit under uncertain atmospheric conditions (night, shade). The purpose of this study is to operate MPP-designed photovoltaic systems under different environmental conditions to achieve superior efficiency and minimize overall system cost and obtain the appropriate voltage and current for efficient battery charging, therefore reducing battery losses and improving life cycle. A prototype 100 kW photovoltaic array was designed, and a boost MPPT controlled PV system with a battery energy storage using a bi-directional DC/DC converter is simulated and studied in the MATLAB/Simulink environment.
A Novel fast improved aquila optimizer based MPPT for standalone PV systems under partial shading conditions
(2022) Aziz, Azaz; Djafer, Mohamed; Chalah, Samira (Supervisor)
In this work, a fast algorithm based on modification sincorporate dint oth erecent Aquila Optimizer is designed for the task of Maximum Power Point Tracking. The introduced algorithm was examined on a standalone PV system subjected to fiv edistinc tcomplicate dpar- tial shading conditions. A comparative study based on efficienc y,robustne ssa ndconvergence speed was carried out on fiv ewel lknow nalgorithms :PSO ,WOA ,GWO ,SS Aan dAO .The results indicate that the proposed Improved Aquila Optimizer (IAO)was in global superior over its competitors,especially in terms of the consumed convergence time. On average, the IOA algorithm was 26.9% faster than PSO, 21.6% faster than GWO and 31.% faster than AO, while maintaining either similar or superior efficiency.