Power

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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.
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.
Application of golden section search MPPT control to grid-connected wind turbine driven PMSG
(2018) Khelifa, Ayoub; Ben Haoua, Seif Eddine; Kheldoun, Aissa (Supervisor)
This project is intended to design a PMSG based wind energy conversion system. Classical controllers, such as P&O MPPT algorithms are employed to ensure maximum power extraction from wind turbine. Speed of the generator is adjusted to match the wind turbine power- speed characteristics. In this project, a new MPPT algorithm is investigated to maximize the power coefficient Cp. The algorithm is based on Golden Section search principle and expected to be faster than the aforementioned algorithms.
Application of golden section search MPPT control to grid-connected wind turbine driven PMSG
(2018) Khelifa, Ayoub; Ben Haoua, Seif Eddine; Kheldoun, Aissa
This project is intended to design a PMSG based wind energy conversion system. Classical controllers, such as P&O MPPT algorithms are employed to ensure maximum power extraction from wind turbine. Speed of the generator is adjusted to match the wind turbine power- speed characteristics. In this project, a new MPPT algorithm is investigated to maximize the power coefficient Cp. The algorithm is based on Golden Section search principle and expected to be faster than the aforementioned algorithms.
Bearing faults classification of induction motor using advanced deep learning techniques
(Université M'hamed Bougara Boumerdès: Institue de génie electronic et electric, 2024) Djelouli, Seyyid Ahmed; Kheldoun, Aissa (supervisor)
This study investigates the application of advanced neural network models for bearing fault detection using vibration and current signals. Bearing faults in induction motors pose significant challenges to industrial operations, often leading to unexpected downtimes and increased main-tenance costs. The study explores the performance of Artificia lNeura lNetwork s(ANN) ,1D- Convolutional Neural Networks (1D-CNN), 1D-CNN with multi-kernel sizes, and Long Short-Term Memory (LSTM) models. Findings indicate that 1D-CNN and its multi-kernel size variant outper-form other models, achieving accuracies up to 99.95% under various load conditions for vibration data. The 1D-CNN multi-kernel size model’s ability to capture diverse features through different kernel sizes proved advantageous, reflectin g asignifica ntimproveme ntov erprevio usmethodologies that relied on extensive preprocessing.For the current signal dataset,Our recent finding ssurpas sall prior results, particularly in variable speed operation, where our work marks a pioneering effort. In our current signal dataset, the pinnacle of accuracy, reaching 99.88%, was attained through the application of the 1D-CNN model with the variable load operation dataset. This remarkable success highlights the effectivenes so fmergin g1D-CN Nwit hVariationa lMod eDecompositio n(VMD) ,en- abling the proficien tdecompositio no fsignal san dresolutio no fboundar yeffec ts tohand leintricate fault patterns. Despite encountering greater complexities in variable speed operation, our models persevered and achieved commendable accuracies. Notably, the 1D-CNN model achieved an accuracy of up to 99.36%. These results highlights the significan tachievemen tmad ei nterm so fdiagnosi si ninduction motors.
Cell nuclei segmentation in histopathology images based on deepneural networks
(2022) Merah, Halima; Zemouri, Nassima; Daamouche, Abdelhamid (Supervisor)
Despite the significant progress in understanding cancer’s biological basis, it continues to confound patients, researchers, and physicians as a self-sustaining and adaptive disease that interacts dynamically with its environment. Analysis of stained tumor sections has a staggering importance in cancer diagnosis and prognosis, which is mainly carried out manually by pathologists. Because most of the human body's billions of cells have a nucleus full of DNA, the genetic code that programs each cell, most analyses begin with identifying the cell's nuclei. Researchers can better grasp the underlying process by identifying the nuclei. They can measure how different samples react to a certain drug. However, huge volumes of medical images make manual analysis challenging, time consuming, and a tedious task. Therefore, other techniques are necessary to automatically analyze large amounts of these complex image data in order to draw biological conclusions from them and to study cellular and tissular phenotypes at a large scale. The automatic segmentation of cell nuclei from this type of image data is one of the bottlenecks for such techniques. We present a fully automated workflow to segment nuclei from histopathology images by using deep neural networks trained from a set of semi automatically annotated image data. In our work, we have used the PSB 2015 crowd-sourced nuclei dataset. We have built three models using three different architectures: U-Net, U-Net++, and a modified version of U-Net architecture.
A comparative study of PSO, Ziegler Nichols, and genetic algorithm-tuned PID and PI controllers for DC motor speed control
(Université M’Hamed Bougara de Boumerdes : Institut de génie electrique et electronique (IGEE), 2024) Touil, Mohammed El Bachir; Boutoura, S.(Supervisor)
A DC motor is widely used for precise speed control in industries, where stability is vital amidst load fluctuations and environmental influences. This study employs Proportional, Integral (PI), and Proportional, Integral, and Derivative (PID) controllers to achieve and maintain desired speeds. Initially, the traditional Ziegler-Nichols (ZN) method is used, followed by optimization of PI and PID parameters using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A Comparative analysis of these methods shows PSO outperforms GA and ZieglerNichols Method.
A Comprehensive study on partial discharge measurements and daignostics
(Université M'hamed Bougara Boumerdès: Institue de génie electronic et electric, 2024) Bencheikh, Younes; Benzaoui, Younes; Bouchahdane, Mohamed (supervisor)
This study aims to explore the applicability and benefit so fpartia ldischarg e(PD)testing for high voltage equipment, particularly in the context of Algeria, where such practices are not yet widely implemented. The scope of this research encompasses various high voltage equipment used in the energy sector. To achieve a thorough examination, we utilized multiple detection methods, including electromagnetic, optical, acoustic, and chemical techniques. Each method was assessed for its effectiveness in identifying and analyzing partial discharge occurrences. Our finding sindicat etha tpartia ldischarg etestin gprovide s amor ecomprehensiv eun- derstanding of insulation integrity compared to traditional methods such as high-pot and high voltage tests. Additionally, PD testing showed excellent compatibility with other insulation assessments like tan delta tests. Through detailed interpretation of PD patterns and comparative analysis, we demonstrated the superior diagnostic capabilities of PD testing. These results suggest that adopting partial discharge testing in Algeria could significantly enhance the reliability and safety of high voltage equipment, contributing to improved maintenance practices and longer equipment lifespan.
Control and energy management of hybrid electric vehicles traction chain based on BLDC motor
(2023) Rahni, Wissem; Bouchanane, Ikram; Ammar, Abdelkarim (Supervisor)
This work focuses on the development of energy management strategy for hybrid electric vehicles (HEVs) using battery and super-capacitors based on fuzzy logic control . The work is divided into two parts. In the firs tpart , an energy management strategy is developed for a hybrid energy storage system (HESS) consisting of batteries and super-capacitors. Fuzzy logic is utilized to intelligently distribute and control power, taking into account factors such as state of charge regulation and degradation of on-board energy sources. This approach ensures optimal utilization of available energy under different circumstances. The second part of the work focuses on motor control in HEVs. Takagi-Suegeno fuzzy logic control is employed to regulate the vehicle’s speed, allowing for adaptive and efficient control in various driving conditions. Simulation tests using MATLAB/Simulink software have been performed in order to validate the proposed techniques. The obtained results will be analyzed and discussed. By integrating fuzzy logic techniques into energy management and motor control, this work contributes to the development of sustainable and efficient transportation systems. The outcomes aim to reduce emissions, improve fuel efficiency, and promote agreener future for the automotive industry.
Control and power quality improvement of three-phase PWM-Rectifier
(2020) Djabali, Sarah; Ait Hamou Ali, Melissa; Ammar, Abdelkarim (Supervisor)
This thesis presents performance enhancement of PWM-rectifiers. Several control strategies have been proposed for the PWM-rectifier, such as voltage-oriented control (VOC), direct power control (DPC) and their combination: DPC with space vector modulation (DPC-SVM). Moreover, various virtual flux estimation techniques have been presented as a sensorless control algorithm in order to rise system reliability by decreasing the number of sensors and improving line-voltage estimation accuracy. Finally, a recent control strategy based on model predictive control has been introduced; this strategy reduces the complexity of the control circuit. All presented techniques have been investigated through simulation using MATLAB/Simulink software. The simulation results validate the presented control strategies. Sensorless predictive control features excellent PWM-rectifier performance as well as high robustness to network distortions.
Control and simulation of an electric vehicle's traction chain based on interior permanent magnet synchronous motor IPMS
(2021) Mokrani, Asma; Oussad, Linda; Ammar, Abdelkarim (Supervisor)
In this work, the concepts of electric vehicles EVs and their traction system is introduced. First by looking to their history, types and different motors used for EVs. Then to go deeply in EV's powertrain, a system main component's modelling (motor, inverter, battery) must be done, where the IPMSM which is the most used motor in EVs applications was chosen. For the inverter , pulse width modulation PWM was first modelled and simulated then because of harmonics problems it was replaced by SVPWM. In a complete agreement the Lithium-ion batteries are the EVs most used battery and this because of their small size, besides many other advantages. Now the next step is to control the modelled traction system for this purpose Indirect Field Oriented control IFOC method was used and since in every control technique there is always a primary control objective combined with a secondary combine objective, the main purpose was to control the angular speed of the motor, then the second objective set is to regulate the flow of current hence, MTPA was integrated to the control system. In order to implement a more robust , nonlinear and chattering free control method , IFOC was replace by a Super Twisting Sliding Mode Control STSMC . As a final improvement to the system a DC-DC converter was added
Control design for trajectory tracking of quadrotor UAV
(2022) Boucherba, Djalal Eddine; Ammar, Abdelkarim
This work studies the modeling and control of the quadcopter. The Newton-Euler method is used to develop the quadcopter's nonlinear dynamic model, and design controllers for attitude and trajectory tracking. Two subsystems describe the motion of the quadcopter; a rotational subsystem for altitude and heading, and a translational subsystem for position. Three controllers were proposed to achieve position tracking, the PID controller, the Fractional Order PID controller and the Sliding Mode controller. The controllers were implemented on the quadrotor model using Matlab/Simulink. Finally, the performance of thE proposed controllers was demonstrated in a simulation study and analysis.
Control design of Puc5-Based multifunction solar filter
(2022) Djelloul, Ammar; Sahli, Mohamed Rafik; Khaldoun, Aissa (Supervisor)
This work presents a Solar Photovoltaic Active Power Filter system (PV-APF)whose architecture basically consisted of a PV generator as primary renewable source three-phase double stage power converters (DC-DC, DC-AC), utility grid and non-linear load connected through Point of Common Coupling (PCC). In addition to the PV power injection the system intended tooperate as an Active Power Filter (APF) to filter the harmonic pollution form the non-linear load. Based on the p-q theory, the PV-APFextracts the fundamental and harmonics information from the polluted load current in order to estimate a compensating reference current that should be injected along with a maximum photovoltaic power extracting (P PV ). Moreover, we have investigated the use of new topology inverter that is thepacked U-cell 5 levels (PUC5) inverter and compared with the conventional two-evel inverter. The comparable simulations of the PV-APF based on each inverterrevealed the superiority of the PUC5 inverter in terms power quality improvement and achieved THD levels. According to obtained results of simulation, under standard test condition (STC) with a disconnected nonlinear load, the classical inverter measures THD of 1.24% in output current, whereas in case of using the PUC5 inverter and under the same conditions the THD was 0.19%.
Control of BLDC motor in a mechanical ventilator
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2023) Esselami, Amina; Touati, Zahia; Bentarzi, H. (supervisor)
This report provides an overview of essential aspects of mechanical ventilation systems, including their operational principles and the mathematical modeling of Brushless DC motors. It specifically emphasizes the utilization of a variable-speed BLDC motor to drive a blower, allowing for the generation of adjustable air pressure levels through the use of a Pulse Width Modulation (PWM) signal to control the supplied voltage. Consequently, this directly influences the motor's speed, which is proportionate to the output pressure. Additionally, a PID controller is employed to attain the optimum required pressure level while minimizing deviations. In this research work, various tools and software are employed, including a designated motor driver and air pressure or flow sensors. These components are utilized to establish a mathematical relationship between input power and output pressure, enabling the implementation of an effective control process for this application.
Control of electric vehicle traction chain based on dual induction motor drine using nine-switch inverter
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2022) Roubache, Karima; Bouarada, Meriem; Ammar, Abdelkarim (Supervisor)
Several research works have been pushed by industry to develop electric vehicles. This study aims at optimizing the cost and energy of the electric powertrain that is based on two in- duction motors supplied by two independent inverters in the conventional case, but this solution is expensive in terms of size and number of power switches. Thanks to its special topology with reduced number of semiconductor switches, the nine switch inverter has been suggested as a substitution to two conventional two level inverters. On the other hand, thanks to the robustness of direct torque control scheme as compared to other vector control methods, (DTC) proves to be a well suited control scheme for electric vehicle applications. Nowadays, the brilliant tech- nology in automotive industry towards regenerative braking is improving, thus bidirectional DC-DC converters are used for capturing the kinetic energy of motor and charging the battery during regenerative mode. In this context, a control of an electric vehicle traction chain based on dual induction motor drive using nine switch inverter is investigated in this report, to study the independency con- trol of nine switch inverter powered motors. Furthermore, the effectiveness of the developed controllers in separating between the flu xan dtorqu econtro lhav ebee nchecked .Likewise ,the regenerative braking has been studied to promote the efficienc yan drealizatio no fenerg ysaving in the electric vehicles. Simulation of these techniques were then carried out using MATLAB/Simulink software and the obtained results analyzed and discussed to confir mth evalidit yo fth epropose dtechniques.
Control of hybrid energy storage system in DC microgrid using energy and power management
(2024) Selmani, Akram; Ouaden, Alaeddine; Boutora, Saliha (supervisor)
The global transition towards sustainable energy solutions has significantly accelerate d the development and integration of renewable energy sources into power systems. Among these systems, DC microgrids have emerged as a promising technology due to their efficiency, reliability, and ability to integrate diverse renewable energy sources such as solar and wind power. This shift is driven by the urgent need to address the environmental impacts of conventional fossil fuel-based energy generation and to enhance energy security. DC microgrids offe rseveral advantages ,including reduced energy losses ,improved power quality, and enhanced flexibility in energy management .These benefits a reparticularly crucial in applications ranging from residential and commercial buildings to remote and off-grid areas. However, the effective operationo fD Cmicrogrid shinge son the integration of robust energy storage systems and advanced energy management strategies. In addition to energy storage, the implementation of sophisticated energy management systems (EMS) is essential for the optimal operation of DC microgrids. These systems employ various control algorithms and optimization techniques to manage the generation, storage, and consumption of energy efficiently.Power management systems (PMS ),which are integral to EMS, utilize artificial intelligence and other advanced methods to enhance the reliability and performance of microgrids.
Control of stand-alone PV system under non-uniform irradiance
(2019) Zerrouki, Nihal; Dahman, Ghenima; Kheldoun, Aissa (supervisor)
Renewable energy sources play vital role in power generation, research in this area has grown rapidly in the last few years and the society is now aware of the harmful effects of fossil fuel on the environment and with the increased cost of fuel production. It is very important to look for alternative efficient, clean and cheap energy sources. Solar energy is usually considered as one of the most promising renewable energy sources. The power generation from the photovoltaic panels is subjected to varying environmental conditions such as temperature and irradiance which lead to a varying conversion efficiency. This necessitates an optimum use of the incident solar radiation. Since a PV unit supplies maximum electrical power only at a certain operating point, Maximum Power Point Trackers (MPPT) were developed to seek this optimal operation under changing light and load conditions. These methods would ensure an efficient and more reliable energy source. This project is intended to simulate and implement a GMPPT for stand-alone PV system in order to provide a fast, efficient tracking solution. PV generators develop a very complex power versus voltage characteristics. That is, under non-uniform isolation, the PV generators exhibit a curve with many power peaks. Identifying the appropriate peak during the operation is the goal of the MPPT for Proper and efficient operation of the PV system, therefore three algorithms are to be studied during this project which are the incremental conductance algorithm one of the most commonly used technique, the golden section search GSS algorithm and the particle swarm optimisation PSO algorithm, a comparison would be done by implementing the algorithms using MATLAB/SIMULINK to find the best MPPT algorithm that converges rapidly avoiding errors and obtaining the best efficiency.
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.
Current sensor fault tolerant control of permanent magnet syschronous machine
(2021) Haouas, Abderaouf; Ghoumrassi, Mohamed Amine; Ammar, Abdelkarim (Supervisor)
To ensure the safety operation and service continuity of control systems, a control technique appeared called Fault-Tolerant Control (FTC). It enables the detection and isolation of faults, as well as the reconfiguration of the control system, to ensure continuity of service and to protect the healthy components from the effects of failing elements. Certainly, several works have been carried out on this subject in different fields of application, among them: the current sensors faults presented in the AC electric drives based on motors like Permanent Magnet Synchronous Motors (PMSMs). These kind of motors are widely used in the industry for variable speed applications due to their high performance reliability and power density. However, in order to achieve fault-tolerant control of current sensors faults , it is necessary to ensure the correct compromise between the detection of faulty sensors and the reconstruction of the currents. In this context, many methods have been proposed for the detection or estimation of three-phase stator currents. Apart from fault detection, current estimation is frequently based either on several cascaded observers, a current sensor and a voltage sensor in the converter DC bus, or an observer and a healthy line current sensor. In this work, the estimation of three-phase stator currents is approached by proposing a method based only on a single current observer, which ensures the estimation and correction of the three stator currents even in case of failure of all current sensors (Current sensorless). This method was then combined with a conventional fault detection and isolation circuit (FDI) and field oriented control (FOC), where the assembly is an active fault-tolerant control for current sensors. Another fault tolerant control method against current sensor faults based on a speed robust controller is named passive fault tolerant control (PFTC). Both methods proposed in this work were applied on an SPMSM. Promising results were obtained in simulation on Matlab/Simulink.
Data analysis and reliability modeling of industrial system
(2019) Boukhedche, Et-Tayib; Laddada, Mohammed; Medjoudj, R. ( supervisor)
Data analysis and reliability modeling are major concerns in degraded systems. Reliability evaluation is done using deterministic or stochastic methods. The aim of this work is to analyze the real data (number of failures, MTBF and failure mechanism) to evaluate the reliability of the equipment used in charging and discharging the boats of Bejaia port.