Doctorat
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/59
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Item Real-time platform for PV panel characterization(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2026) Touabi, Cilina; Ouadi, Abderrahmane(Directeur de thèse)Accurate characterization and modelling of PV panels are essential for performance analysis, control, and optimization of PV systems. Although, manufacturers provide PV panels characteristics under Standard testing conditions. These latter vary over time due to environmental change and PV module degradation, making PV parameter estimation a critical challenge. This work develops a real-time platform for PV panels characterization based on an electronic circuit and a programmed microcontroller interfaced with LabVIEW, enabling automatic acquisition of I–V and P–V characteristics under real operating conditions. The system acquires and stores the PV and weather data for modelling. The work focuses on parameter identification of the Lumped model using metaheuristic optimization techniques, including Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Killer Whale Optimization (KWO). GWO is embedded in the platform for real-time parameter extraction, and two improved algorithms (IOB-PSO) and (MQOB-KWO) are proposed to enhance estimation accuracy, developed in MATLAB and validated with experimental data under varying conditions, the proposed methods demonstrate improved performance. The proposed platform and enhanced algorithms offer a reliable and cost-effective solution for PV modelling, performance assessment, and renewable energy system optimization.Item Contribution à l’analyse et à la synthèse des systèmes de commande intelligents à structure évolutive(Universite M'Hamed Bougara Boumerdès : Faculté des Hydrocarbures et de la Chimie, 2026) Lamraoui, Oualid; Habbi, Hacene(Directeur de thèse)This thesis focuses on data-based process control strategies and, in particular, addresses the Robust Evolving Cloud-based Controller (RECCo). Starting from the basic RECCo protocol, originally developed for SISO systems, the thesis investigates its extension to multivariable and coupled systems by adopting a decentralized control approach. The proposed strategy relies on the idea of neatly arranging a control scheme that combines RECCo sub-controllers acting independently on each loop, using only the received measurements without incorporating any process model or decoupling structure. The proposed synthesis approach is based on separability in both the action and adjustment of the controllers, thereby providing it with strong practical motivation. For the experimental validation of D-RECCo, applications to a pilot heat exchanger and a laboratory three-tank interacting system (DTS-200) are presented in this thesis. The optimization of the RECCo controller is the second problem addressed in this work. In this context, an enhanced cloud removal mechanism (RECCo-CRM) is developed and experimentally validated on the DTS-200 system. Based on several practical considerations, the objective was to eliminate outdated information during the evolving process while preserving control stability and performance. The thesis is enriched with several simulation and experimental results, collectively demonstrating the potential of the proposed approaches.Item Power quality enhancement in smart metering systems(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2026) Khaldi, Bouchra Feriel; Dekhandji, Fatma Zohra(Directeur de thèse)The reliability of residential power systems depends on the effective detection and classification of power quality disturbances (PQDs), including voltage sags, swells, interruptions, and harmonics. This thesis presents a low-cost smart energy meter integrating embedded artificial intelligence for real-time power quality and energy monitoring. An RMS-based detection algorithm implemented on an ESP32 enables rapid anomaly detection, with local alerts via LEDs and buzzer and remote notifications through Blynk IoT and email. Several signal processing and machine learning approaches are investigated, including RMS analysis, Continuous Wavelet Transform (CWT), Short-Time Fourier Transform (STFT), 1D-CNN, 2D-CNN, and a hybrid 2D CNN-LSTM model. The proposed 2D CNN-LSTM achieves a maximum classification accuracy of 98.75% across five datasets under noise levels ranging from 0 to 30 dB, demonstrating strong robustness. For embedded deployment, lightweight models are optimized for ESP32 hardware, with a compact 1DCNN model requiring only 2 KB of memory. The final embedded AI model occupies 32 KB and performs disturbance classification every 4 seconds. The complete smart meter system is implemented at a total cost of approximately €52, providing a practical solution for residential applications. The results demonstrate that accurate power quality monitoring and classification can be achieved using low-cost embedded AI systems for future smart grid integration.Item Advanced control of wind driven permanent magnet synchronous machine(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2026) Mechri, Hadjira; Kheldoun, Aissa(Directeur de thèse)This thesis explores advanced control and fault detection strategies for wind energy conversion systems driven by Permanent Magnet Synchronous Generators (PMSG). The work focuses on improving the reliability of grid integration through the application of Model Predictive Control (MPC) to a Three-Level Neutral Point Clamped (3L-NPC) inverter. A novel open-circuit fault detection and localization method is proposed to identify faulty switches and clamping diodes in 3L-NPC inverter . By applying current predictions within the MPC framework, the strategy enables rapid and accurate fault detection without additional sensors or complex computations. It can be achieved in less than one fundamental period which will allow the system to tolerate the fault rapidly. Beyond detection, the use of MPC demonstrates fault-tolerant capability, particularly in handling clamping diode failures, thereby maintaining system performance under faulted conditions. The proposed approach is validated through extensive simulations and hardware-in-the-loop (HIL) experiments. Results confirm that combining predictive control with integrated fault detection significantly improves the performance and dependability of wind-driven PMSG systems. This research addresses critical challenges in renewable energy conversion, contributing to the advancement of reliable and resilient wind energy integration into modern smart grids.Item Mise en ouvre d'une solution dédiée à la sécurité de l'information(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Besmi, Fatma Zohra; Belkacem, Samia(Directeur de thèse)La protection des données médicales est devenue un défi crucial à l'ère numérique, car les avancées technologiques et la diffusion généralisée de l'information ont rendu cette tâche plus complexe. Cette thèse aborde le conflit inhérent entre la nécessité de traiter les signaux médicaux efficacement et la nécessité de maintenir la confidentialité des patients. Notre objectif est de concevoir et de développer des solutions de sécurité de l'information applicables spécifiquement pour la transmission, le stockage et le traitement sécurisé des données d'électrocardiogramme (ECG). Ce travail présente nos principales contributions visant à renforcer la confidentialité de ces données sensibles. Nous commençons par une analyse exhaustive des techniques de chiffrement existantes pour l'ECG, en identifiant leurs forces et leurs limites. Sur la base de cette revue, nous proposons trois solutions cryptographiques fondées sur le chiffrement homomorphe (HE). La première solution intègre de manière fluide l'algorithme de détection du complexe QRS de Pan et Tompkins avec une technique de chiffrement homomorphe partiel (PHE) basée sur l'algorithme RSA modifié (MRSA). Dans le but d'accroître la complexité cryptographique et la résistance à la factorisation des nombres premiers, notre algorithme MRSA détermine le module à l'aide de trois nombres premiers aléatoires générés ( , , et ) via un générateur congruentiel linéaire (LCG). Il est important de noter que les clés publiques et privées du système utilisent au lieu de , ajoutant ainsi une couche supplémentaire de sécurité. La deuxième approche constitue une version améliorée du système cryptographique homomorphe MEHC (Modified Gorti-Enhanced Homomorphic Cryptosystem). Dans ce travail, nous avons optimisé le système EHC (Gorti-Enhanced Homomorphic Cryptosystem) en modifiant sa procédure de génération de clés à l'aide d'un LCG afin de produire de grands nombres premiers. De plus, nous avons augmenté la valeur du module et élargi l'espace des messages, renforçant ainsi la résistance du système aux attaques par factorisation. Pour optimiser le processus de chiffrement, nous avons utilisé la quantification et la représentation en virgule fixe. Comme couche de sécurité supplémentaire, un processus d'évaluation a été intégré à l'algorithme proposé, permettant d'effectuer diverses opérations mathématiques de manière homomorphe sur les données chiffrées, plutôt que sur les données originales. Cette approche garantit non seulement un chiffrement efficace des signaux ECG, mais permet également une détection fiable des arythmies telles que la bradycardie et la tachycardie sans compromettre la confidentialité des données La dernière contribution est un cadre hybride de chiffrement ECG, appelé DNA-MEHC, qui combine le codage basé sur l'ADN avec notre système MEHC afin d'obtenir une sécurité renforcée tout en réduisant le temps de calcul. Dans cette approche, les signaux ECG bruts sont binarisés, codés en séquences ADN, convertis en flux d'entiers, puis sécurisés par MEHC. Chaque étape est entièrement réversible pour permettre une reconstruction précise des signaux. La méthode DNA-MEHC a été validée à l'aide de signaux ECG issus des bases de données AHA et MIT-BIH Arrhythmia, démontrant sa robustesse et sa capacité de généralisation sur des ensembles de données hétérogènes. Les méthodes proposées ont été évaluées à l'aide de métriques de performance telles que la précision (Acc), la sensibilité (SE), l'erreur quadratique moyenne (MSE), le rapport signal sur bruit (SNR), le temps d'exécution, l'analyse des histogrammes, la corrélation et la valeur prédictive positive (PPV). Les résultats montrent que nos approches offrent un moyen fiable de concilier une sécurité robuste des données et des processus diagnostiques efficaces, représentant ainsi une avancée significative dans le domaine de la confidentialité en santé numériqueItem Fatigue behavior analysis of blade incidence angle flight link rods(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Chellil, Sadek; Nour, Abdelkader(Directeur de thèse)The significance of this thesis lies in the application of the XFEM method to the fatigue analysis of a helicopter's main rotor pitch links, which are responsible for transmitting motion from the swashplate to the blade. This research combines both experimental and numerical methods to systematically investigate the defect-position and size study validated by experiments on AISI 1045 steel on the pitch links. We conducted tensile and fatigue tests on specimens made of AISI 1045 steel to provide data for validating the numerical model. Furthermore, we used a three-dimensional finite element method in ABAQUS to evaluate residual stresses and deformations, and finally, we determined the SIF by applying XFEM to a fatigue crack in the rod. The variation of SIF with crack length enabled the prediction of fatigue life based on Paris's law. At the beginning of this work, we performed a heat treatment, finding that tempering is crucial for improving the mechanical properties of AISI 1045 steel by relieving internal stresses. While tempering at 350 °C yields the optimum tensile strength (approximately 1490 MPa), using higher temperatures, up to 550 °C, significantly reduces this strength (by up to 36\%). In the second part, the results successfully confirmed the accuracy of the numerical model in simulating the experimental stress-strain curve. Fatigue tests showed that XFEM effectively matched numerical predictions, highlighting the influence of stress state and cyclic load levels on fatigue life. Crack position and depth were linked to the number of cycles to failure and SIF variations. Following this study, the AISI 1045 steel model was deemed suitable for a helicopter main rotor control rodItem Développement d'un système d'aide au diagnostic intelligent pour la gynécologie(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Boumeridja, Hafida; Ammar, Mohammed(Directeur de thèse)Aujourd'hui, l'inégalité face au diagnostic prénatal est directement exacerbée par l'écart technologique qui sépare les structures de soins dans les milieux à ressources limitées. L'utilisation d'échographes obsolètes limite la détection d'anomalies fœtales, obligeant le clinicien à interpréter des images trop dégradées pour être fiables. Le remplacement des infrastructures étant fortement entravé par des contraintes budgétaires persistantes, l'exploration de solutions d'amélioration par l'intelligence artificielle représente la voie indispensable pour rétablir l'équité des soins. Face aux enjeux socio-économiques et au coût des équipements, cette recherche explore l'efficacité de la Super-Résolution par IA pour améliorer les échographies fœtales dégradées en milieux limités. Cette démarche vise à "enrichir" l'information visuelle existante, offrant ainsi une seconde vie au matériel obsolète et une meilleure équité de soins aux patientes. Nous avons mis à l'épreuve plusieurs architectures de ''Deep Learning'' afin d'en mesurer la robustesse et la pertinence diagnostique. Le cœur de la méthodologie réside dans une évaluation croisée : aux tests statistiques s'est ajoutée une expertise clinique approfondie. Les résultats valident la capacité de ces méthodes à augmenter la certitude diagnostique, offrant ainsi une perspective de performance équitable sans dépendre de la modernité du matérielItem Study and design of an antenna system for wireless terminals(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Zidour, Ali; Ayad, Mouloud(Directeur de thèse)To meet the escalating demand for high-speed real-time connectivity, 5G New Radio (NR) systems have transitioned to the millimeter-wave (mmWave) spectrum. Despite this spectrum offers wide bandwidth, signal propagation is limited by severe path loss and attenuation, necessitating high-gain, beamforming phased antenna arrays. This thesis details the development of mmWave phased arrays featuring robust beam-scanning capabilities, emphasizing endfire configurations that operate across multiband operation for 5G NR requirements while ensuring spherical coverage compliance with 3GPP standards. To address current design challenges, this research introduces two novel endfire antenna arrays: (1) a horizontally polarized wideband antenna array with low form factor, and (2) a shared-aperture dual-band vertically polarized antenna array. Both designs feature compact size, low complexity, and high performance. The design methodology, simulation, fabrication, and experimental validation are thoroughly discussed. Measurement results align well with simulations, demonstrating superior merits in terms of impedance bandwidth, aperture efficiency, port isolation, gain, and symmetric radiation patterns for wide beam scanning applications. Finally, it evaluates practical considerations related to PCB integration and real-world environmental interactionsItem Remaining useful life estimation of critical industrial equipment by using a data driven approaches(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Amar Bouzid, Abir; Benazzouz, Djamel(Directeur de thèse)In the face of fierce competition in the industrial sector, manufacturing technologies have evolved considerably, from simple to complex and sophisticated systems. Machining processes cover a wide range of manufacturing systems and play a crucial role in production. Monitoring the condition of these systems through an effective maintenance strategy is therefore essential for guaranteeing production reliability and quality; however, due to their complexity, focusing on monitoring critical components is generally more practical and efficient, as their degradation has a serious impact on the entire system. Maintenance practices have undergone significant evolution, giving rise to the emergence of the concept of Prognostics and Health Management (PHM), which introduces a predictive approach in Condition-Based Maintenance (CBM). Estimating components Remaining Useful Lives (RULs) is one of the most important aspects of PHM to track their degradation and predict how long they can operate before failing. However, to achieve accurate RUL estimations, it is essential to process the raw data efficiently. Sensor-derived data often contains noise, irrelevant information, and inconsistencies that can mask essential insights. Therefore, it is crucial to conduct appropriate data processing to extract the relevant features, or Health Indicators (HIs), that reflect the system's behavior. This data refinement not only improves the accuracy of predictive models but also increases their robustness, enabling manufacturers and maintenance managers to schedule replacements, minimize unplanned downtime, and extend machine life cycles. This is the background to the present study, which proposes a novel methodology for estimating the RUL. In the context of PHM data-based methods, also known as "data-driven methods," the main objective of this thesis is to design relevant health indicators HIs capable of reflecting the degradation behavior of critical components and estimating their RULs. Initially, a new time-frequency analysis technique called Empirical Wavelet Packet Decomposition (EWPD) was introduced. This method uses a new segmentation of the signal's Fourier spectrum, which is spread over several levels to improve the structural performance of conventional methods. Subsequently, a novel health indicator HI, is developed upon the basis of an innovative selection of time-domain features for each frequency band at each level. Lastly, the RULs of the monitored components are estimated using the Long Short-Term Memory (LSTM) network. The proposed methodology is implemented on a selection of CNC milling cutters from the "Prognostics Data Challenge 2010" databaseItem Acceleration of arithmetic computations in elliptic curve cryptography(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2026) Nait-Abdesselam, Fadila; Khouas, Abdelhakim(Directeur de thèse)The Elliptic Curve Digital Signature Algorithm (ECDSA) is a fundamental cryptographic mechanism for ensuring the authenticity and integrity of digital communications. A central operation in signature verification is double point multiplication (DPM), whose computational structure directly affects performance, memory consumption, and resistance to side-channel attacks (SCAs). This thesis proposes simple and uniform constant-time algorithms for DPM based on an iterative left-to-right windowing method that performs simultaneous recoding and evaluation in a single pass. This design improves efficiency, reduces memory requirements, and strengthens protection against timing and power-analysis attacks. The proposed methods are analyzed through precise analytic formulas covering speed, memory, and security, and are compared with state-of-the-art approaches over NISTrecommended curves, as well as twisted-Edwards and Montgomery models. Unlike curve-specific techniques, the proposed algorithms are field-independent and flexible, enabling practical trade-offs between speed, memory, and security. When applied to ECDSA, the algorithms reduce point additions without increasing point doublings and require minimal precomputation, resulting in significant computational savings compared to existing methods