Mourad Yessef

This study investigates the critical influence of bed-packing architecture on the transient fluid and phase-change kinetics of a latent heat packed-bed thermal energy storage (TES) system. Utilizing a validated two-dimensional computational fluid dynamic model, the charging behavior of an insulated tank containing spherical phase change material (PCM) capsules is systematically analyzed. The thermocline behavior during charging and discharging is examined. The system performance is evaluated under three distinct structural packings: ϵ = 0.36, ϵ = 0.49, and ϵ = 0.60. The simulation results demonstrate that increasing porosity from 0.36 to 0.60 accelerates the charging speed by 20.8%, accompanied by a 33.3% increase in the rate of temperature rise in the HTF and PCM phases. However, the system efficiency was decreased by 0.5%. A porosity of ϵ = 0.49 is recommended as the optimal bed configuration, balancing a high volumetric heat density with a 97.5% charging completion rate and providing a high efficiency of 44.5%. These findings establish clear design guidelines for optimizing the thermal efficiency of packed bed latent TES systems.

Grid-code compliance has become a critical requirement for the reliable integration of large-scale wind farms into modern transmission networks. This study presents a comprehensive grid-code compliance assessment of a fully operational 120 MW wind farm connected to the Moroccan high-voltage transmission network. Unlike many previous studies that focus on isolated compliance requirements, the proposed assessment simultaneously evaluates power-quality performance, frequency tolerance, Low Voltage Ride Through (LVRT) capability, reactive power support, and short-circuit behavior under representative operating conditions. The analysis is based on detailed power-system modeling, manufacturer-certified technical data, and applicable grid-code requirements. The results demonstrate full compliance with the prescribed limits for flicker emission, harmonic distortion, voltage unbalance, frequency tolerance, and LVRT performance. However, the assessment identifies a reactive power compensation limitation at the Point of Common Coupling (PCC), particularly under low active-power operating conditions, where the wind farm is unable to fully satisfy the reactive power absorption requirements specified by the grid code. The analysis further indicates that this limitation is mainly associated with the existing reactive compensation configuration and network characteristics. These findings provide practical insights into the behavior of large-scale wind farms connected to transmission networks and highlight the importance of integrated compliance assessments for identifying technical limitations that may affect long-term grid integration and operational flexibility.

This study presents an EEG-based Brain-Computer Interface for intuitive robotic navigation driven by ocular activity. A real multi-subject dataset collected from 15 participants was used to extract blink- and gaze related EEG features for five control commands. Eight supervised classifiers were evaluated under stratified 5-fold cross-validation: Random Forest, multilayer perceptron, support vector machine, k-nearest neighbors, RUSBoost, Naive Bayes, decision tree, and linear discriminant analysis. Among them, Random Forest achieved the best overall performance, reaching 98.74 ± 1.19% accuracy, 0.9874 Macro-F1, and 0.9993 macro-AUC, demonstrating excellent robustness and class separability across the five ocular classes. The decoded commands were transmitted wirelessly to an embedded Raspberry Pi platform, where they were converted into safe motor actions for wheelchair type robot navigation. Real world experiments in indoor environments confirmed stable motion control, reliable command execution, and successful obstacle avoidance without physical interaction from the user. These findings support the feasibility of a low-cost, portable, and non-invasive BCI solution based on realistic multi-subject EEG data for assistive mobility applications. Although the full control loop operated online, the end-to-end system latency was not quantitatively benchmarked in the present study and remains an important limitation to be addressed in future work. Future developments will focus on expanding the command set, improving robustness under more variable conditions, and validating the system in broader assistive mobility scenarios.

This paper presents a new maximum power point tracking (MPPT) method for photovoltaic (PV) systems based on artificial neural networks (ANNs) models integrated with incremental learning and the capability of real‐time acquiring meteorological conditions by using a Raspberry Pi. The approach uses hourly irradiance and temperature values obtained from the NASA POWER API to dynamically re‐train the ANN model according to weather conditions. Simulation results using MATLAB/Simulink reveal that the proposed system exhibits high precision and robust performance over a range of irradiance (200–1000 W/m ² ). The system achieved an average PV conversion efficiency of 99.52% and load‐side efficiency of 98.50%, outperforming several conventional and intelligent MPPT techniques reported in the literature. Performance quantification results in an MSE of 0.0024 and an R ² value of 0.9987, revealing the excellent regression accuracy for the Vmp. Apart from the accuracy, the system exhibits a low response time and good power tracking under various operating circumstances.

Wind energy conversion systems (WECS) exhibit highly nonlinear behavior due to the stochastic nature of wind speed variations. In such conditions, the main objective is to maximize the extracted power while ensuring stable system operation. This paper presents a nonlinear control strategy for a grid-connected WECS based on a permanent magnet synchronous generator (PMSG). A backstepping control strategy (BSC) is applied to regulate both the machine-side converter (MSC) and the grid-side converter (GSC). The proposed controller aims to improve wind energy extraction under variable wind conditions while ensuring smooth operation of the PMSG with satisfactory static and dynamic performance. The effectiveness of the proposed approach is first evaluated through simulation studies conducted in the MATLAB/Simulink environment. The results obtained show improvement in effectiveness in terms of reference tracking, response time, overshoot elimination, accuracy, and mitigation of power ripple. Additionally, the feasibility of the proposed BSC is experimentally verified through real-time implementation on a dSPACE DS1104 board.