Browsing by Author "Grainat, Youcef"

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    Data aggregation point placement optimization in Smart Metering Networks
    (JES, 2024) Grainat, Youcef; Recioui, Abdelmadjid; Oubelaid, Adel
    This study explores the application of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) within the framework of smart grids (SG), specifically for the optimal placement of data aggregation points (DAPs) across a network of 150 Z-wave smart meters distributed within various smart cities. The investigation aims to identify which of the two- optimization strategies offers a more cost-efficient solution while evaluating their performance in terms of transmission average latency (AL) and execution time (ET) efficiency. The results indicate that although ACO slightly edges out PSO in reducing overall costs in networks with a higher complexity and more DAPs, PSO demonstrates superior performance in execution speed, lower AL, and total cost, underscoring its viability for swift integration in smart metering infrastructures.
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    Digital Filters for reducing the effects of dispersion in optical communication
    (2016) Grainat, Youcef; Mebarki, Yaser; Zitouni, Abdelkader (Supervisor)
    The aim of this project is to use digital filters to reduce the effects of dispersion in modern WDM optical communication systems at two transmission speeds, 2.5 and 5 Gbps. For this purpose two types of filters [Gaussian Filter (GF) and Cosine Roll Off Filter (CROF)] have been tested by simulation using OptiSystem and Matlab in single mode fiber links at the operating wavelength of 1550 nm. From the obtained results a comparative study with respect to the conventional Dispersion Compensating Fibers (DCF) has been carried out.
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    Optimization of smart grid communication systems
    (Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Grainat, Youcef; Recioui, Abdelmadjid(Directeur de thèse)
    This PhD research focuses on optimizing smart grid communication systems through the application of metaheuristic optimization algorithms, specifically Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), as well as advanced communication technologies such as Multiple-Input Multiple-Output (MIMO) and LoRa. The study aims to improve the reliability, efficiency, and security of real-time data exchange in critical smart grid components, including smart metering, home control, and Wide-Area Monitoring Systems (WAMS). In the first part, PSO and ACO are employed to optimize the placement of Data Aggregation Points (DAPs) in networks of 150 Z-wave smart meters deployed across various smart cities, with results showing that PSO provides faster execution, lower latency, and better cost-efficiency compared to ACO, especially in less complex networks. The second part introduces MIMO communication to improve data transmission accuracy and speed within WAMS, demonstrating performance gains in latency, data completeness, and correctness when compared with traditional systems. In the final phase, LoRa technology is utilized to support long-range, low-data-volume transmission for a proposed Wide-Area Network State Monitoring System. Using the IEEE 14-bus system with Phasor Measurement Units (PMUs), the study compares Single-Input Single-Output (SISO) and MIMO configurations under varying Signal-to-Noise Ratios (SNRs), revealing that MIMO significantly reduces the Bit Error Rate (BER) and that higher reporting rates further enhance data accuracy. Overall, the findings demonstrate the effectiveness of optimization and advanced communication techniques in building a more resilient, cost-effective, and high-performance smart grid communication infrastructure