Mémoires de Master 2

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Author: search.filters.author.Kheldoun, Aissa (Supervisor)Start date: 2015

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    Sizing of PV Pumping System with Optimizing MPPT Algorithm
    (Université M’Hamed Bougara de Boumerdes : Institut de génie electrique et electronique (IGEE), 2024) Benmbarek, Malik; Daif, Mouhaned; Kheldoun, Aissa (Supervisor)
    This report covers a comprehensive study of the sizing, modeling and simulation of a stand-alone solar water pumping system in El Oued, Algeria. The initial part involves selecting a farm located in Hassi Khalifa, El Oued, Algeria, which has an average water consumption of 43m/h. The irrigated site covers 1 hectare (10,000 square meters) of land. Sizing such a system has been carried out using differen ttool ssuc ha sCropwat ,Climwat ,an dPVgis. Sizing has led to the selection of a 2.2 kW submersible pump, 3.19 kW PV array, 3 kW inverter, and a 74m2 Tank of height 3 m. System dynamic modeling is done using MATLAB/Simulink which contains several models of sub-systems such as solar arrays, DC-DC boost converter, two-level inverter, Squirrel cage IM, and centrifugal pump, This dynamic modeling has been developed based on the sizing of the system. To improve further the overall system’s efficienc y,an enhanced P&O MPPT algorithm has been developed. Moreover, a DTC algorithm is used to regulate both the motor’s speed and torque. Various numerical simulations were conducted to illustrate and validate the effectivenes so fthi ssystem .MATLAB/Simulin ksimulation shows that this system can deliver the required energy needed for the farm to satisfy all the requirements.
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    Control of stand-alone PV system with global maximum power point identification
    (Université M’Hamed Bougara de Boumerdes : Institut de génie electrique et electronique (IGEE), 2024) Damou, Rezkallah; Saheb, Anis; Kheldoun, Aissa (Supervisor)
    As the world faces the depletion of fossil fuels and the adverse environmental impacts of their use, renewable energy sources have become crucial for sustainable development. Solar energy, one of the most abundant renewable resources, is harnessed using photovoltaic (PV) systems that convert sunlight into electrical energy. Despite their potential, PV systems are plagued by low efficiency and dependency on various factors such as solar irradiance, temperature, electrical load, and ambient conditions. One of the major challenges in PV systems is partial shading, which occurs when only a portion of the PV array is obstructed from sunlight. This shading can drastically reduce the overall power output and create multiple local maximum power points (LMPs) on the power curve, complicating the optimization process. In PV systems with partial shading, multiple LMPs and one global maximum power point (GMPP) exist. Hence, the identification of global maximum power point GMPP is needed, which is the main topic of this thesis. The project's method is applied and simulated using MATLAB and Simulink on a stand-alone photovoltaic system powered by an MPPT controller. The suggested method (Enhanced Adaptive P&O) produced outstanding results in differentiating between uniform irradiance and partial shading occurrences under a variety of insolation levels and complex shading scenarios. A comparative study based on convergence time, and efficiency is conducted along with other well-known techniques: Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). The obtained results demonstrated that the EA-P&O is either excellent or competitive with respect to tracking efficiency, convergence speed and eliminate the oscillation problem.
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    Evolutionary algorithms-based optimal preventive maintenance scheduling of power systems generators
    (Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2023) Elhazati, Bouchra; Kheldoun, Aissa (Supervisor); Belagoune, S.
    This thesis addresses the optimal Generators preventive-Maintenance Scheduling (GMS) problem in electric power systems that includes several machines. This problem can be solved using a variety of ways, such as metaheuristic methods and mathematical programming. The problem is formulated as a mathematical optimization model using mathematical programming techniques, and the best solution is then found using algorithms. Simulating the maintenance schedule allows you to assess its effectiveness while modeling the equipment and its failure behavior. Metaheuristic methods entail creating maintenance schedules utilizing generalizations or subject-matter expertise. The primary objective of this thesis is to contribute to the performance improvement of a discrete evolutionary algorithm for a reliable and extremely accurate optimization of the discrete objective functions in order to address the issue of the best preventive maintenance scheduling of power systems generators. For planning the generator preventative maintenance, a modern metaheuristic algorithm named "the Discrete Mayfly Optimization (DMFO)" has been designed. This algorithm was proposed as an innovative swarm intelligence optimization algorithm in 2020, it combines the advantages of several existing optimization algorithms. his algorithm has been used in several applications including industrial optimization, ensemble forecasting system, and photovoltaic systems. A First-Bit Flip and Shift (FBFS) strategy for binary vectors, which is a process of manipulating binary vectors, has been first proposed to improve the performance of evolutionary algorithms. The FBFS strategy is a local search strategy that performs small changes to the obtained solutions to help evolutionary algorithms in local optimization and avoiding them from getting stuck in local optima. The proposed technique has been evaluated on a 21-unit test power system with a peak power load demand of 4739 MW in three cases where the total number of the workers available per week is limited. The improved algorithm showed at the end its effectiveness to find a solution for the GMS problem where the Sum of Squares of the Reserves (SSR) of generation is minimized. The results are compared to previous works that used other metaheuristic techniques in order to evaluate the performance of the proposed FBFS-DMFO algorithm and its search process in solving power system GMS problem.
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    Analysis of frequency response of the Algerian power system
    (Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2023) Hamam, Lyes; Ykhlef, Abdessemed Abdelwaheb; Kheldoun, Aissa (Supervisor)
    The frequency response of a power system is a key parameter that reflects its stability and ability to maintain a consistent supply of power. This project presents an analysis of the frequency response characteristics of the Algerian power system, with a focus on frequency control techniques, reserve capacity, flywheel battery and battery energy storage system (BESS) integration. Furthermore, this analysis explores the role of reserve capacity in frequency regulation. Reserve capacity is spare generation capacity that stands by to deal with sudden frequency excursions or imbalances between supply and demand. The assessment focuses on the adequacy of reserve capacity in the Algerian grid and its impact on frequency stability. Finally, the project considers the integration of Battery Energy Storage Systems (BESS) and flywheel battery as a potential solution to improve frequency response capability.
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    Identification and control of asynchronous motor using meta-heuristic algorithms
    (2023) Ghernaout, Rayane; Kheldoun, Aissa (Supervisor); Belmadani, Hamza
    The present study is centered on the examination, regulation, and enhancement of induction motors (IMs) through the application of meta-heuristic algorithms. The aim of this study is to optimize the performance and efficiency of induction motors (IMs) in various applications. The study begins with the formulation of a mathe- matical model for induction motors (IMs). Subsequently, meta-heuristic algorithms, namely EO, RSBA, and JAYA, are employed to determine the parameters of the IM.The estimation of parameters is conducted by utilizing the inputs of measured stator voltages, currents, and rotor speed. This study focuses on the modeling of indirect rotor flux-oriented control(IRFOC )and the utilization of the resulting IM param- eters to identify the motor. Control gains are then optimized through the imple- mentation of RSBA and JAYA algorithms. The findings of the simulation indicate that the system’s performance has been enhanced in comparison to conventional manual tuning techniques. The project acknowledges the difficulties involved in the optimization process and emphasizes the significance of meticulous parameterse- lection. In summary, this study serves as a valuable contribution to the progression of IM technology, highlighting its potential to enhance performance and efficiency in industrial settings.
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    Desing and implementation of electric vehicles bidirectional charger
    (2023) Ghodbane, Anouar Cherif; Malki, Youcef Islem; Kheldoun, Aissa (Supervisor); Khettab, Soufian
    This project aims to enhance the design and implementation of a bidirectional electric vehicle (EV) charger, a vital component in modern energy systems. The bidirectional charger not only facilitates charging and discharging of energy to and from the grid or energy storage systems but also enables efficient energy management and utilization. Additionally, it can serve as a shunt active power filter ,effectively mitigating harmonics generated during power conversion, thereby reducing total harmonic distortion (THD) and ensuring grid reliability. To regulate the operation of this power electronic device, two control techniques were investigated: hysteresis current control (HCC) and model predictive control. Through comprehensive simulation studies, the results obtained from both techniques were presented and compared. Furthermore, real-time evaluations were conducted to validate the efficacy of the employed control techniques. By improving the bidirectional EV charger’s design and employing advanced control strategies, this project contributes to the development of more efficient and reliable energy systems while addressing power quality concerns through harmonic reduction.
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    Optimization of AGC parameters in interconnected power systems using enhanced marine predators algorithm
    (2023) Ferdjallah, Cheima; Mokri, Silia; Kheldoun, Aissa (Supervisor)
    In this work, an objective function considering performance indices and an enhanced marine predator algorithm ( EMP A ) are proposed to optimize the parameters of a Fuzzy - PID controller for Automatic Generation Control of interconnected power systems. Ini- tially, a two-area non-reheat unit was implemented, and the gains of five different controllers were adjusted to verify the suitability of MP A employing IT AE objective function in solving AGC issues. The superiority of the proposed MP A based F - P ID controller has been demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as water cycle algorithm ( W CA ) based F - P ID controller for the same interconnected power system. EMP A - F - P ID method which integrates EMP A and F - P ID controller is tested in a typical two-area non-reheat system, and sys- tem responses reflect the advantages of the proposed objective function and EMP A . Then, sensitivity analysis is carried out to identify the closed-loop system’s robustness. A two-area non-reheat unit was also tested under the generation rate constraints ( GRC ) nonlinearity. To guarantee the suitability of the proposed MP A - F - P ID , a model with a mixture of power plants, such as reheat, hydro, and gas units, in each area was carried out with and without the HV DC link, which can increase practical issues with AGC . The proposed controller’s robustness was studied for all models under numerous scenarios, including step load perturbations ( SLP ). Simulation results proved that the proposed MP A - F - P ID provided superior performance compared to recently reported techniques in terms of peaks and settling time.
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    Analysis and design of an analytical MPPT applied for different converter configurations
    (2022) Djouider, Abderrazak Ziane; Tahir, Mustapha; Kheldoun, Aissa (Supervisor)
    This project is intended to analyze the maximum power point tracking constraint conditions of the 12 PV systems under ideal conditions; The constraint conditions are necessary and sufficient conditions to guarantee the existence of the maximum power points of these PV systems. These constraint conditions are expressed by the modal parameters of the PV, therefore they show the inherent relationship between the load and the cell parameters when the maximum power point of the system always exist. In addition to apply them in practice, the maximum power point tracking constraint condition of the practical application are nvestigated. Furthermore, our work includes the variable weather parameter maximum power point tracking method based on equation solution (ES-VWP method). This equation consists of two analytic equations which represent two different operating points of the PV system. The simulation of this technique has shown a good effectiveness in terms of maximum power point and time response.
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    Droop-controlled single phase AC micrigrid using SOGI-PLL
    (2022) Diaf, Hadil; Kheldoun, Aissa (Supervisor)
    A microgrid (MG) is defined as a low or medium voltage distribution network that is also connected to a number of distributed generation sources, energy storage elements, and con- trollable loads. A MG can operate both in grid-tied mode, when its bus is connected to the main grid, or in islanded more, when it’s disconnected. MG control strategy is considerably different than that of the traditional grid. A hierarchi- cal control architecture consisting of primary, secondary, and tertiary control is adopted to regulate voltage and frequency and ensure a stable and reliable network. In order to offer a good power quality for end users in islanded microgrids, the power sharing between distributed generators (DGs) is the first challenge that must be dealt with. In this report, a droop control method based primary control for two parallel single-phase VSIs forming an islanded microgrid is presented. The controller aims to ensure optimal power sharing between different DGs using mod- ified droop-control technique and introducing SOGI-PLL for parameter estimation and DC- offset rejection, during islanded operation.
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    Optimal placement and sizing of distributed generators in Bechar’s electric distribution network
    (2022) Benslimane, Kenza; Bougrid, Assia; Kheldoun, Aissa (Supervisor)
    Optimal allocation of distributed generators (DG) in the distribution system have a significant influence on the network performance. An optimization based on DG allocation an dsizingfor minimizing active power loss and annual operation cost is applied to Bechar’s distribution network. Meta-heuristic techniques are proposed to solve the optimization problem with the aim of improving the technical and economical benefits .The distribution system power flow is solved usingt he Open DSS software, and the optimization method is created in Matlab. Grey Wolf Optimizer (GWO) shows a better and faster convergence compared to Genetic Algorithm (GA). The optimization aims to place and size distributed energy resources (DERs)(i.e., gas turbines, Photovoltaic systems ) over the nine (9) electric substations of Bechar’s electric network to minimize active power loss and reduce annual energy cost along with saving fuel resources. In order to investigate the impact of integrating DGs, a simulation of the optimization results is performed. Active power loss is reduced along with reducing dependency on the grid where the annual saving in fossil fuel is increased.