Mohamed Elbar

In order to improve power quality in low-voltage distribution networks with nonlinear, distorted, and unbalanced loads, this research suggests an innovative design for Shunt Active Power Filters (SAPFs). Compared to conventional single-inverter SAPF structures, the improved method uses a combination of two T-Type three-level inverters operating in a parallel configuration to improve compensator performance, leading to a higher current-carrying capacity as well as better harmonic reduction and system scalability. The Synchronous Reference Frame (SRF) algorithm is used to extract reference currents in order to achieve the required accuracy of harmonic cancellation. In order to ensure both a quick response and a suitable switch state selection for compensatory current references, the Model Predictive Current Control (MPCC) technique is employed. To maintain the DC-link voltage at a steady level and guarantee its correct operation under rapidly fluctuating loading conditions, a Proportional Integral (PI) controller-based DC–DC converter is also utilized. Four real-time operational circumstances are used to verify the performance of the proposed method using MATLAB/Simulink R2023a (i) SAPF activation under nonlinear loading conditions, (ii) dynamic load variation, (iii) distorted and unbalanced operation, and (iv) grid voltage disturbances including sag and swell conditions. The simulation study’s results show that, in all of the previously indicated scenarios, the source current Total Harmonic Distortion (THD) is reduced and an almost unity power factor is maintained while maintaining a constant DC-link voltage. Furthermore, the obtained performance meets IEEE-519-2022 requirements, demonstrating the feasibility of the suggested SAPF with two inverters under high-load circumstances.

Maintaining wind turbine blades is a challenging task, often marked by high costs, safety risks, time inefficiency, and the possibility of incorrect diagnosis. A promising approach to support preventive maintenance involves the use of drones and deep learning for inspection and early fault detection. This paper offers a comparative study of five advanced deep learning models: Residual Networks (ResNet), Inception Networks (InceptionV3), YOLO (You Only Look Once), Efficient Net, and Vision Transformers (ViTs) applied to the DTU Drone Inspection Images of Wind Turbine dataset. The objective is to determine the most effective model for detecting defects in wind turbine blades, which is critical for ensuring the efficiency and safety of wind energy systems. The models were assessed using key performance metrics, such as accuracy, precision, recall, F1‐score, and inference time. Efficient Net emerged as the top performer with the highest accuracy and balanced efficiency, whereas YOLO demonstrated the fastest inference time, making it ideal for real‐time applications. ResNet and InceptionV3 also performed well, particularly in detecting subtle defects, whereas ViTs showed strong capabilities in capturing complex global features despite longer processing times. The findings provide valuable insights for selecting appropriate deep learning models in drone‐based wind turbine inspections, contributing to more effective and reliable maintenance practices.

Commutation failures (CFs), which occur when current transfer between valves in line-commutated converter high-voltage direct current (LCC-HVDC) systems is disrupted, pose a challenge in weak alternating current (AC) networks. This paper introduces a coordinated control strategy that combines a fuzzy self-tuning proportional-integral (PI) controller (FSTPIC) and a static synchronous compensator (STATCOM) device to mitigate CFs and enhance system stability. The approach applies the FSTPIC to both converters of the HVDC link, while the STATCOM at the inverter side delivers dynamic reactive power and voltage support during AC faults. We test this strategy on the CIGRE HVDC benchmark system using MATLAB/SIMULINK simulations. The results demonstrate that the proposed method significantly reduces CFs, mitigates transient oscillations, and shortens recovery time compared to conventional control techniques. This coordinated control boosts voltage stability and the system’s ability to ride through faults, confirming its superiority under various fault scenarios in weak-grid conditions.

In this work, we report a protective encapsulation intended as the final coating layer on solar cells. The formulation consists of polylactic (PLA)-based resin, modified with hexadecyltrimethoxysilane (HDTMS), epoxidized polybutadiene (EPB), and benzotriazole as a UV absorber with approximate weight fractions ranging from 20 to 60 wt% for PLA, 30–80 wt% for solvents (toluene and chloroform), and 0–5 wt% for HDTM, EPB, and benzotriazole with percentages 54.2%, 29.2%, and 16.7%, respectively. The encapsulating material, due to its insulating nature and high optical transparency, surpasses that of ethylene–vinyl acetate (EVA), as demonstrated in this study. To assess the protective effect of the developed formulation, the study focused on applying the modified PLA resin onto isolated methylammonium lead iodide (MAPI) perovskite films on glass substrates. The samples were prepared as isolated MAPI absorbers to specifically assess the intrinsic contribution of the dual encapsulation configuration at its real position in a complete solar cell stack, demonstrating that even this unoptimized perovskite film exhibits remarkable stability and excellent structural and optical retention over two months under the protective scheme (86% of its initial structural stability, as quantified from integrated XRD peak intensities, and 68% of its initial optical absorbance, determined from the integrated UV–Vis spectra), whereas the uncoated films showed significant degradation. Although MAPI was selected as a model system due to its well-known environmental instability, the proposed encapsulation material and methodology are not limited to this architecture and can, in principle, be applied to various photovoltaic technologies. These findings demonstrate the strong potential of the polylactic-based resin as an effective environmental barrier for solar cells and provide a solid foundation for future full-device integration studies.

For the failure prediction in structures, it is essential to accurately estimate the geometric parameters, especially the crack. This paper presents a numerical simulation of eddy current testing (ECT) using the finite element method (FEM) to estimate the crack angle in a 2D axisymmetric cracked tube. FEM simulation was done for different crack angles, revealing a monotonic increase in normalized impedance with increasing crack angle. The relation between the sensor impedance and crack angle was expressed by a tangential fitting function with a high coefficient of determination ( R ² = 0.99991) and low RMSE (0.167°). The fitted function can be employed as a direct technique to estimate the angle of a hidden crack for the sensor impedance. As a supportive alternative, the gray wolf optimizer (GWO) was used to optimize the crack angle using a measured normalized impedance value. The efficiency of the proposed technique was tested across seven crack angle cases covering the full range from 0° to 90°, with both methods achieving relative errors below 1% in all cases, demonstrating the reliable behavior of the proposed approach.

In this article, we have studied the double‐star permanent magnet synchronous machine, and two mathematical models have been proposed in Park reference frame: the d ₁ q ₁ d ₂ q ₂ O ₁ O ₂ model and the dqz ₁ z ₂ O ₁ O ₂ model. This machine is controlled by vector control on both models. This is followed by the estimation of the speed and the position of the machine by two techniques; the first is the estimation of the back EMF of the model using fuzzy logic and then the determination of the speed and the position using PLL. And the second is model reference adaptive system (MRAS) observer speed of the machine. The simulation of the sensorless control of the machine is carried out under the MATLAB/Simulink software environment and gives encouraging results.

A mathematical model of a double‐star permanent magnet synchronous motor is proposed, coupled with an estimator for speed, position, and currents based on an extended Kalman filter. This filter is optimized using a novel methodology. The primary objective is to determine the optimal values for the three noise covariance matrices to ensure the convergence of the parameter estimation. To enhance the estimator’s performance, several innovative optimization strategies are introduced. These combine different techniques, notably particle swarm optimization with the Nelder–Mead simplex algorithm, as well as a genetic algorithm coupled with this same hybrid method. Other approaches are also deployed, such as a fuzzy self‐tuning method for success‐history–based parameter adaptation for differential evolution, along with a fuzzy version of the linear population size reduction success‐history–based adaptive differential evolution algorithm. Furthermore, an accelerated procedure for initializing the covariance matrices is implemented. Specifically, the error estimation covariance matrix and the measurement noise covariance matrix are fixed, while the process noise covariance matrix is the subject of the optimization. The validity of the proposed approach is demonstrated through comprehensive numerical simulations. These simulations include the machine model, its power supply, and the parameter estimator, all implemented within the MATLAB/Simulink environment.

The investigation focused on chalcogenide perovskite compounds, specifically CaZrTe₃ and CaHfTe₃. We analyzed their structural, electronic, and optical properties using first-principles calculations with the CASTEP code, employing gradient generalized approximations of Wu and Cohen (GGA-WC) and Perdew et al. (GGA-PBE) from density functional theory (DFT). Our findings revealed that CaZrTe₃ and CaHfTe₃ compounds have orthorhombic crystalline structures, with primitive cell volumes measuring 661.30 and 665.04 ų, respectively, using the PBE method. Our analysis of the electronic characteristics suggests the potential for customizing bandgap and semiconductor behavior, which could improve the efficiency of electronic semiconductor devices and photovoltaic cells. Additionally, the optical properties of CaZrTe₃ and CaHfTe₃ perovskites, including high absorption coefficients and distinct reflectivity characteristics, indicate the potential for improved solar energy conversion efficiencies and advanced optoelectronic device performance. However, the study also highlights challenges in the commercial deployment of chalcogenide perovskite materials, such as scalable synthesis methods and integration into existing device architectures. We propose future research directions focusing on developing and exploring material interfaces within device configurations. This research contributes to our understanding of CaZrTe₃ and CaHfTe₃ chalcogenide perovskites and underscores their potential to significantly influence the development of photovoltaic and optoelectronic technologies and their environmental compatibility.