Publikační činnost Centra energetických jednotek pro využití netradičních zdrojů energie (9370)

Permanent URI for this collectionhttp://hdl.handle.net/10084/89109

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Centra energetických jednotek pro využití netradičních zdrojů energie (9370) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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Now showing 1 - 20 out of 397 results

A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images
(Springer Nature, 2024) Ahmed, Maqsood; Zhang, Xiang; Shen, Yonglin; Ali, Nafees; Flah, Aymen; Kanan, Mohammad; Alsharef, Mohammad; Ghoneim, Sherif S. M.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy. In this paper, we propose a transfer learning CNN framework for classifying air temperature levels from human clothing images. The framework incorporates various deep transfer learning approaches, including DeepLabV3 Plus for semantic segmentation and others for classification such as BigTransfer (BiT), Vision Transformer (ViT), ResNet101, VGG16, VGG19, and DenseNet121. Meanwhile, we have collected a dataset called the Human Clothing Image Dataset (HCID), consisting of 10,000 images with two categories (High and Low air temperature). All the models were evaluated using various classification metrics, such as the confusion matrix, loss, precision, F1-score, recall, accuracy, and AUC-ROC. Additionally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) to emphasize significant features and regions identified by models during the classification process. The results show that DenseNet121 outperformed other models with an accuracy of 98.13%. Promising experimental results highlight the potential benefits of the proposed framework for detecting air temperature levels, aiding in weather prediction and environmental monitoring.
SSO optimized FOFPID regulator design for performance enhancement of doubly fed induction generator based wind turbine system
(Springer Nature, 2024) Dembri, Rafik; Rahmani, Lazhar; Babes, Badreddin; Zaini, Hatim G.; Ghoneim, Sherif S. M.; Bojer, Amanuel Kumsa; Flah, Aymen; Sharaf, Ahmed B. Abou
A wind turbine system (WTS) is a highly coupled and nonlinear system where the output power depends upon highly uncertain wind speed. Therefore, the quality of produced power becomes a challenging problem for researchers. Direct Vector Control (DVC) is a powerful and widely utilized power control strategy to deal with winds that vary rapidly and randomly. As a result, this article employed the newly developed Social Spider Optimization (SSO) technique to optimize the design parameters of Fractional-Order Fuzzy Proportional-Integral with Derivative (FOFPID) regulator to maintain the output power of the studied DFIG-based WTS at the rated value under dynamic wind conditions. The suggested FOFPID controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the DVC strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show that the FOFPID regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, Total Harmonic Distortion (THD), and Weighted Total Harmonic Distortion (WTHD). The suggested FOFPID regulator also displays stronger robustness against parameters mismatch and weather change than other regulator architectures.
Sensorless finite set predictive current control with MRAS estimation for optimized performance of standalone DFIG in wind energy systems
(Elsevier, 2024) Mebkhouta, Toufik; Golea, Amar; Boumaraf, Rabia; Benchouia, Toufik Mohamed; Karboua, Djaloul; Bajaj, Mohit; Chebaani, Mohamed; Blažek, Vojtěch
This paper introduces a sensorless control strategy combining Finite-Set Predictive Current Control (FSPCC) and Model Reference Adaptive System (MRAS) estimation to enhance the performance of standalone Doubly-Fed Induction Generators (DFIG) in wind energy systems. Addressing the challenges of cost and reliability, the proposed approach eliminates mechanical speed sensors by employing MRAS for real-time rotor speed and position estimation. FSPCC predicts rotor current one step ahead (K + 1), enabling precise control, optimal switching state selection, and improved current regulation with reduced ripple. The significance of this study lies in its potential to advance standalone wind energy systems by providing a robust, efficient, and reduced cost and effective solution for sensorless operation. The proposed strategy was experimentally validated using a 3 kW DFIG coupled with a turbine emulator, connected to a three-phase resistive load, and managed via a DS1104 control board. The system was tested under diverse operational conditions, including sudden load variations and dynamic speed changes, simulating real-time wind energy scenarios. The results demonstrate exceptional robustness and adaptability, with accurate speed estimation, effective voltage regulation, stable current waveforms, and enhanced power quality. The system also exhibited improved reactive power handling, ensuring smooth transitions under fluctuating loads and mitigating power oscillations. By addressing critical challenges in standalone DFIG applications, this work highlights the importance of integrating FSPCC and MRAS as a promising control solution. The results confirm its potential to improve system stability, efficiency, and reliability, offering significant advancements in renewable energy technologies and optimizing the performance of wind energy conversion systems. Also, this combination isn't applied before in the field in can be applied in many other fields like electric vehicles, robotics, aerospace systems and marines.
Enhanced wombat optimization algorithm for multi-objective optimal power flow in renewable energy and electric vehicle integrated systems
(Elsevier, 2025) Nagarajan, Karthik; Rajagopalan, Arul; Bajaj, Mohit; Raju, Valliappan; Blažek, Vojtěch
In this study, the authors propose the Enhanced Wombat Optimization Algorithm (EWOA) as a solution for the optimal power flow (OPF) issue that occurs in transmission networks. With the incorporation of different types of uncertainties like wind energy, solar photovoltaic (PV) systems, and plug-in electric vehicles (PEVs), the conventional OPF was made to undergo transformation as a stochastic OPF. In order to enhance the method's diversity, a Levy flight mechanism was integrated into the algorithm. For this study, the OPF problem was developed as a Multi-Objective Optimization (MOO) problem with the following objectives such as active power loss, emissions and generation cost. Then, the authors deployed the Monte Carlo simulations to determine the generation costs incurred upon wind energy, solar PV, and PEV sources. This was done so to reduce the overall costs and also overcome the system issues like feasibility and affordability. Further, the authors also used Weibull, lognormal and normal probability distribution functions (PDFs) for characterizing the uncertainties faced in solar PV, wind energy and PEV sources. In various scenarios, the proposed method was validated for its efficacy on IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. This was done so to demonstrate its capability and address the complexities involved in OPF problem under different conditions. The key advancement of the proposed EWOA is that it integrates the Levy flight mechanism and chaotic sine map, which in turn dramatically boost its optimization capabilities. These mechanisms further contribute to optimal outcomes in terms of less active power loss and low operation costs and emissions. To be specific, the proposed EWOA attained the finest outcomes in terms of generation cost ($731.41/h) and 0.1989 ton/h for emissions in the altered IEEE 30-bus system, $35,642.53/h for cost and 0.8683 ton/h for emissions in the altered IEEE 57-bus system, and $127,753.82/h for cost and 33.2763 MW for real power loss in the altered IEEE 118-bus system. In line with the outcomes, the EWOA presented in this study exhibits strong convergence characteristics and effectively explores the Pareto front. In summary, the EWOA method surpasses the standard WOA outcomes by providing superior exploration capabilities, rapid convergence, robust constraint management, and low sensitivity to variations in the parameters. These advantages make EWOA an effective solution for tackling optimal power flow and other such complex multi-objective optimization challenges.
Techno-economic optimization and sensitivity analysis of off-grid hybrid renewable energy systems: A case study for sustainable energy solutions in rural India
(Elsevier, 2025) Kumar, Pujari Harish; Alluraiah, N. Chinna; Gopi, Pasala; Bajaj, Mohit; Kumar, P. Sunil; Kalyan, CH. Naga Sai; Blažek, Vojtěch
In the twenty-first century, global energy consumption is rapidly increasing, particularly in emerging nations, hastening the depletion of fossil fuel reserves and emphasizing the vital need for sustainable and renewable energy sources. This study aims to analyze hybrid renewable energy systems (HRESs) that use solid waste to generate power, focusing on difficulties linked to intermittent renewable sources using a techno-economic framework. Employing the HOMER Pro software, prefeasibility analysis is performed to meet the energy needs of an Indian community. System architecture optimization depends on factors like minimizing net present cost (NPC), achieving the lowest cost of energy (COE), and maximizing renewable source utilization. This study evaluates the technical, economic, and environmental feasibility of a hybrid renewable energy system (HRES) comprising a 400-kW solar photovoltaic (PV) array, a 100-kW wind turbine (WT), a 100-kW electrolyzer, 918 number of 12V batteries, a 200-kW converter, a 200-kW reformer, and a 15-kg hydrogen tank (H-tank). This optimal configuration has the lowest NPC of $26.8 million and COE of $4.32 per kilowatt-hour, and a Renewable Fraction (RF) of 100 %. It can provide a dependable power supply and satisfy 94 % of the daily onsite load demand, which is 1080 kilowatt-hours per day. The required electricity is sourced to load demand entirely from renewable energy at the given location. Additionally, the study highlights the benefits of HRES in solid waste management, considering technological advancements and regulatory frameworks. Furthermore, sensitivity analysis is conducted to measure economic factors that influence HRES, accounting for fluctuations in load demand, project lifespan, diesel fuel costs and interest rates. Installing an HRES custom-made to the local environmental conditions would provide a long-lasting, reliable, and cost-effective energy source. The results show that the optimal HRES system performs well and is a viable option for sustainable electrification in rural communities.
Optimal design of a novel modified electric eel foraging optimization (MEEFO) based super twisting sliding mode controller for controlling the speed of a switched reluctance motor
(Springer Nature, 2024) Das, Debiprasanna; Sahu, Binod Kumar; Pati, Swagat; Mohapatra, Bhabashis; Sitikantha, Debashis; Bajaj, Mohit; Blažek, Vojtěch; Prokop, Lukáš
Switched Reluctance Motor (SRM) has a very high potential for adjustable speed drive operation due to their cost-effectiveness, high efficiency, robustness, simplicity, etc. Now a days SRMs are widely used in automotive industries as traction motors in electric vehicles and hybrid electric vehicles, air-conditioning compressors, and for other auxiliary services. In this article, a novel super twisting sliding mode controller (STSMC) is proposed to improve the performance of an SRM for reducing the ripple in speed and torque. Initially, a novel Modified Electric Eel Foraging Optimization (MEEFO) technique is developed by incorporating a quasi-oppositional phase and its performance is compared with the conventional Electric Eel Foraging Optimization (EEFO) technique with four popular benchmark functions. Then, both MEEFO and EEFO techniques are implemented to optimally design PI, SMC and STSMC controllers to effectively control the speed of an SRM. The study is carried in three different scenarios such as during starting, during a torque change and during a speed change. Finally, performance of the SRM in real time is studied with OPAL-RT 4510 simulator. It is observed that MEEFO based STSMC exhibits significant improvements in effectively controlling speed of the SRM, as compared to its other proposed counterparts.
Entropy-weighted medoid shift: An automated clustering algorithm for high-dimensional data
(Elsevier, 2025) Kumar, Abhishek; Ajani, Oladayo S.; Das, Swagatam; Mallipeddi, Rammohan
Unveiling the intrinsic structure within high-dimensional data presents a significant challenge, particularly when clusters manifest themselves in lower-dimensional subspaces rather than in the full feature space. This complexity is prevalent in real-world datasets, such as text documents and images, which often contain numerous noisy or sparse features. Traditional clustering methods often overlook these latent subspace structures. This paper introduces a novel subspace-based clustering algorithm designed explicitly to address this challenge. Building upon the robust medoid shift framework, we integrate a dimensionality reduction scheme that dynamically projects data onto evolving subspaces determined through entropy-constrained optimization. This approach effectively filters irrelevant information and identifies underlying clusters, optimizing subspace representation while avoiding trivial solutions. Unlike existing methods, our algorithm ensures convergence without necessitating stopping criteria, thereby enabling efficient processing of large datasets. We validate the efficacy of our approach through extensive experiments on synthetic and real-world datasets, demonstrating substantial performance enhancements over state-of-the-art techniques. By explicitly uncovering the underlying subspace structures, our method opens new avenues for effective high-dimensional data clustering and offers valuable insights into complex data environments.
Development and enhancement of metamaterial-inspired Ag-GaAs THz MIMO antenna with optimized diversity metrics using data-driven machine learning algorithms for future 6G networks
(Springer Nature, 2025) Armghan, Ammar; Mandaliya, Vishalkumar; Alsharari, Meshari; Aliqab, Khaled; Ben Chaabane, Slim; Flah, Aymen
The MIMO antenna design is specifically engineered to support optimized performance in emerging 6G networks. Utilizing advanced techniques such as metamaterials and machine learning algorithms, the antenna system achieves high data rates, improved diversity, and robust signal reliability, making it ideal for next-generation ultra-fast and intelligent wireless communication technologies. Our advanced metamaterial configuration demonstrates high gain and bandwidth. A low ECC value 0.0004 shows minimal correlation, ensuring better signal diversity and improved system performance. Similarly, a high diversity gain confirms the antenna's efficiency in maintaining robust signal reception under varying conditions. The CCL values of 0.0916 bits/Hz bits/Hz provide insight into the information-carrying capacity of the MIMO configuration. The MIMO antenna design achieves a maximum gain of 8.9 dBi and a wide bandwidth of 30 THz. This performance is attained through a combination of parametric optimization and machine learning techniques, enhancing both efficiency and operational range. The machine learning algorithms used for optimization yield a high R-2 value of 0.99, indicating excellent prediction accuracy. The proposed antenna, featuring metamaterial characteristics, demonstrates strong potential for next-generation 6G networks, offering enhanced performance, efficiency, and compact design integration.
Comparative analysis of bulk ceramics and thick film coatings for optimized energy storage technologies
(Springer Nature, 2024) Khan, Imran Hussain; Habib, Muhammad Salman; Maqbool, Adnan; Rafiq, Muhammad Asif; Ali, Amjad; Nur, Khushnuda; Inam, Aqil; Nasimullah; Blažek, Vojtěch; Mišák, Stanislav
The present investigation provides an easy and affordable strategy for fabrication of functional ceramics Bi0.5Na0.5TiO3-SrFe12O19 (BNT-SrF5) thick films on a flexible, inexpensive and electrically integrated substrate using electrophoretic deposition process (EPD). EPD is a widely accepted, environmentally friendly method for applying coatings from a colloidal suspension to conductive substrates. Lead-free ferroelectric BNT-SrF5 powder was synthesized by solid state method to fabricate bulk samples and thick films (30-160 mu m) by EPD process. Thick films were deposited onto nickel substrate by applying EPD parameters, i.e. voltage (225-290 V) and coating time (30-180 s) to acetone based colloidal suspension without aid of any dispersing agent. In a comparative analysis, both thick films and bulk ceramics revealed significant densification with sintering temperature from 1025 to 1150 degrees C. Fourier transform Infrared (FTIR) and X-ray diffraction (XRD) analysis revealed presence of distorted perovskite structure following calcination and sintering processes. Scanning electron microscopy (SEM) provided the surface morphologies of BNT-SrF5 powder. The dielectric constant of film sample revealed more thermal stable response compared to the bulk ceramics. Impedance spectroscopy explained the electrically active regions and hopping conduction mechanism which witnessed NTCR behavior. The potential applications for the miniaturization of electronics are sensors, actuators and energy harvesting devices.
Graphene-TiN-Fe2O3-W metasurface solar absorber: A computationally optimized ultra-broadband design for scalable solar-thermal and renewable energy applications
(Elsevier, 2026) Arulkumar, S.; Kumar, U. Arun; Flah, Aymen; Kraiem, Habib
The urgent demand for efficient renewable energy solutions has accelerated progress in solar absorber technologies; however, many existing designs remain constrained by limited spectral bandwidth, angular sensitivity, and fabrication complexity. This study introduces a multi-material metasurface solar absorber that achieves an unprecedented ultra-broadband operation spanning 0.20-3.00 mu m (bandwidth approximate to 3000 nm) with exceptional angular tolerance up to 80 degrees. The proposed architecture integrates a titanium nitride (TiN)-coated square resonator, ferric oxide (Fe2O3) dual circular rings, and tungsten (W)/yttrium aluminum garnet (Y3Al5O12) cylindrical resonators on a graphene-enabled tunable metasurface, supported by silicon dioxide and silicon nitride substrates. This material hybridization enables absorption efficiencies exceeding 99.9 % across wide incidence angles while preserving fabrication feasibility. To further enhance the performance, machine learning based optimization using an XGBoost algorithm is employed for multi-objective design exploration, achieving high predictive accuracy (R2 = 0.9743) in modeling angular response. Electromagnetic simulations confirm that the absorber's superior performance arises from synergistic plasmonic-dielectric hybridization, which excites multiple resonant modes to broaden the spectrum. Comparative benchmarking against existing solar absorbers highlights the proposed design's superiority in both bandwidth and angular robustness. By integrating advanced materials engineering, electromagnetic optimization and machine learning driven design strategies, this work develops a new platform for next-generation solar energy harvesting. Furthermore, the reliability and scalability of the proposed absorber make it suitable for deployment in diverse solar thermal applications including industrial process heating, domestic water heating, agricultural crop drying and residential space heating systems.
Internal friction and flowability of clay powder depend on particle moisture, size and normal stress
(Elsevier, 2024) Prokeš, Rostislav; Jezerská, Lucie; Gelnar, Daniel; Zegzulka, Jiří; Žídek, Martin
This work investigates the impact of changes in the moisture content of bulk materials. The regularity of moisture change in bulk materials was evaluated for three different particle size classes of clay powders. The research measures the angle of internal friction and flowability under four different normal loads, reflecting the varying pressures during bulk material storage. The influence of moisture change in bulk materials was most pronounced for the smallest particle size fraction, where even a very small moisture change in the order of tenths of a percent steeply affected flowability due to internal friction. After increasing the moisture content by a few percent, a steady state of flow occurred. Critical value was determined when water in the bulk material caused liquefaction. For larger particle size fractions, the impact of moisture change was evident only at higher values (12.5%), with no liquefaction occurring even at 30% moisture. The change in normal load, on the other hand, affected particles of larger size fractions, resulting in improved flow properties.
Enhancing PEM fuel cell efficiency through bio-inspired MPPT under variable operating conditions
(Elsevier, 2026) Kebbab, Fatima Zohra; Bajaj, Mohit; Blažek, Vojtěch; Prokop, Lukáš
The aim of this paper is to develop and evaluate a nature-inspired metaheuristic strategy for Maximum Power Point Tracking (MPPT) strategy in Proton Exchange Membrane Fuel Cells (PEMFCs), whose efficiency is highly sensitive to dynamic operating conditions such as cell temperature and the partial pressures of hydrogen and oxygen. These fluctuations continually shift the system's Maximum Power Point (MPP), necessitating adaptive control methods to maintain optimal power extraction. This study introduces a novel MPPT technique based on the Horse Herd Optimization Algorithm (HOA), a recent bio-inspired metaheuristic modeled on the social behavior of horse populations. To the best of our knowledge, this work presents the first application of HOA to PEMFC systems. A comprehensive dynamic model is constructed, integrating the electrochemical characteristics of a 50 kW PEMFC stack, a DC-DC boost converter, and an adaptive MPPT controller guided by HOA. The algorithm adjusts the converter's duty cycle by mimicking behavioral mechanisms-such as grazing, hierarchy, sociability, imitation, defense, and roaming-organized across age-based groups to enhance convergence speed and accuracy. The effectiveness of the HOA-based MPPT is benchmarked against the Cuckoo Search Optimization (CSO) method under various conditions, including standard operation, temperature variations (328 K to 348 K), and pressure fluctuations (1.0-2.0 atm). Simulation results using MATLAB/Simulink demonstrate that the HOA algorithm achieves superior performance, with a maximum power point tracking efficiency of 99.7 % compared to 99.64 % for CSO. Additionally, HOA exhibits a significantly faster settling time of 0.0570 s, outperforming CSO's 0.12 s, and maintains comparable rise times (0.0016s) while eliminating voltage and current oscillations. Under varying thermal and pressure conditions, HOA demonstrates exceptional robustness, rapid convergence, and high stability, maintaining optimal power delivery where conventional methods degrade. This work represents the first successful integration of the Horse Herd Optimization Algorithm into MPPT control for PEM fuel cells and demonstrates its superiority over both traditional and intelligent techniques. It offers a highly efficient and adaptive solution, with promising prospects for future scalability and deployment in real-world fuel cell energy management systems.
Reliability-oriented framework for UAV-based inspection missions in modern power and energy systems
(Springer Nature, 2025) Al-Haddad, Luttfi A.; Khalid, Wissam; Tariq, Sarmad Ziyad; Mrah, Muhannad M.; Flah, Aymen; Tazay, Ahmad F.; Jaber, Alaa Abdulhady
Ensuring mission reliability is vital for the autonomous deployment of unmanned aerial vehicles (UAVs) in modern power and energy systems, particularly under spatial and operational constraints. This study presents a data-driven classification method that assesses the reliability of UAV-based inspection missions by identifying whether individual mission locations are suitable, at risk, or infeasible based on spatial and operational parameters. Leveraging the Cumulative UAV Routing Problem (CUAVRP) benchmark, four representative mission scenarios were analyzed, each characterized by unique UAV fleet sizes, sensor ranges, and endurance limits. Synthetic stress nodes were introduced to emulate edge-case conditions encountered in infrastructure inspection tasks. Each node was classified based on three categorical targets: Mission Feasibility, Coverage Reliability, and Deployment Suitability. A gradient boosting classification model was trained on spatial and operational features to determine node status. Evaluation across all scenarios yielded consistently high performance, with the cuavrp_d9_k6_r800 scenario achieving 97.05% accuracy, 96.33% precision, 97.72% recall, and 97.02% F1-score. Furthermore, incorporating physical-layer degradation factors such as signal attenuation, multipath fading, and interference is expected to enhance the realism of future reliability assessments and improve classification robustness. The proposed classification framework supports intelligent mission planning, enhances operational resilience, and facilitates automated UAV deployment strategies in critical inspection environments within the power and energy sector.
Design of a distributed power system using solar PV and micro turbine-based wind energy system with a flywheel energy storage
(Springer Nature, 2025) Bhavani, Tharinaematam; Rajababu, Durgam; Irfan, Muhammad; Rakesh, T.; Sekhar, P. Chandra; Flah, Aymen; Kraiem, Habib
As renewable energy sources gain distinction in distributed power generation, micro-grid systems integrating solar photovoltaic (PV), micro-turbine-based wind energy, and flywheel energy storage have developed as sustainable solutions. This paper presents a novel design methodology for a hybrid micro-grid system that optimally integrates these components, ensuring enhanced efficiency, resilience, and stability. In a grid outage or weak-grid scenario, a flywheel provides instant backup until wind/solar/storage catches up. The distributed nature ensures that local power supply is maintained, thereby reducing blackout risks. Flywheels avoid chemical waste, unlike batteries. The proposed hybrid micro-grid system represents an innovative approach to distributed power generation in terms of triple energy sources and storage type is in the form of mechanical and the response speed is ultra fast (few milli seconds), fast response time (milliseconds), ideal for voltage/frequency regulation Handling sudden load changes or source fluctuations high reliability due to multiple backups and high sustainability. This hybrid system is suitable for decentralized operation, which allows each unit to make local decisions. This research contributes to advancing micro-grid technology, supporting the transition towards sustainable and resilient energy infrastructures. A key contribution of this work is the design of a fuzzy logic controller (FLC) for dynamic energy management and control of DC-DC converters. Advanced control algorithms (like fuzzy logic, manage real-time source prioritization, power quality regulation, and energy storage control). It enables multi-input, multi-output decision-making that traditional PID or rule-based controllers can't handle efficiently. Handles nonlinear, variable, and uncertain conditions better than conventional methods. Comparative analysis reveals that the FLC outperforms conventional PID controllers, offering a significantly faster dynamic response and reducing output ripples to a greater extent. This leads to improved power quality, enhanced system life, and optimized energy utilization.
Synergistic Ni-Mn ferrite/rGO nanocomposites for high-performance supercapacitors
(Springer, 2025) Beloev, Hristo I.; Beloev, Ivan H.; Iliev, Iliya K.; Kumar, Ravinder; Najser, Jan; Frantík, Jaroslav; Saini, Meenu; Kumar, Pawan
To address the growing demand for advanced energy storage systems, Ni-Mn ferrite (Ni-Mn(1-x)Fe2O4) nanoparticles integrated with reduced graphene oxide (rGO) were synthesized via a sol-gel-assisted co-precipitation method. Different concentration ratios of (Ni-Mn(1-x)Fe2O4)/ rGO have been tried with sample codes NMR33, NMR50, and NMR67 for optimization of supercapacitive performance. This hybrid approach aimed to synergistically enhance the electrochemical performance by improving electrical conductivity, surface area, and structural integrity. Comprehensive structural and morphological analyses, conducted using XRD, FESEM, FTIR, and TGA, confirmed the successful formation of the nanocomposites. Electrochemical evaluations, including cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) in a 6 M KOH electrolyte, revealed that the NMR50 composition exhibited the highest specific capacitance of 250 F/g at 1 A/g and retained 97% of its capacitance after 10,000 charge-discharge cycles. This superior performance is attributed to the optimized Ni/Mn ratio and the strong interfacial coupling between the redox-active ferrite phase and the conductive rGO matrix, which facilitates efficient electron transport and ion diffusion. The results underscore the potential of Ni-Mn ferrite/rGO nanocomposites as promising electrode materials for next-generation supercapacitor applications.
Calculating torque, back-EMF, inductance, and unbalanced magnetic force for a hybrid electrical vehicle by in-wheel drive application
(Springer Nature, 2024) Hosseinpour, Alireza; Rahideh, Akbar; Abbas, Ahmed; Iqbal, Atif; El-Bayeh, Claude Ziad; Flah, Aymen; Ali, Enas; Ghaly, Ramy N. R.
To use a Hybrid Excitation Synchronous Machine (HESM) in a hybrid electrical vehicle (HEV), its performance indicators such as back-EMF, inductance and unbalanced magnetic force should be computed preferably by an analytical method. First, the back-EMF is calculated by considering alternate-teeth and all-teeth non-overlapping and overlapping windings. The effects of three types of magnetization patterns including the radial, parallel and Halbach magnetizations on the back-EMF waveform have also been investigated. Then, the self-inductance of the stator and rotor windings, the mutual inductance between the stator and rotor windings, and the mutual inductance between the stator phases are computed. Next, the components of the unbalanced magnetic force (UMF) in the direction of the x and y axes and its amplitude are computed. Moreover, the effects of the magnetization patterns on those magnetic pulls are investigated. To minimize the UMFs, symmetry must be implemented in the excitation sources; therefore, first the stator winding then the permanent magnet and rotor winding are modified in such a way that the UMFs are reduced. Increasing the temperature leads to a weakening of the magnet's residual flux density, which strongly affects the performance characteristics of the electric machine such as Back-EMF and UMF. Finally, the ratio of the permanent magnet flux to the rotor flux is determined in such a way that the average torque is maximized. In this section, the effects of three magnetization patterns will be investigated.
Optimal structure to maximize torque per volume for the consequent-pole PMSM and investigating the temperature effect
(IEEE, 2024) Hosseinpour, Alireza; Abbas, Ahmed; Sadegh, Mahmoud Oukati; Iqbal, Atif; Flah, Aymen; Prokop, Lukáš; Ali, Enas; Ghaly, Ramy N. R.
Heat removal, maximizing torque, minimizing losses, volume, cost, and temperature effect play essential roles in electrical vehicle applications. An inner-rotor consequent-pole permanent magnet synchronous machine (CPPMSM) merits suitable losses, cost, and heat rejection. Hence, first, a two-dimensional model of CPPMSM is explained based on solving Maxwell's equations in all regions of the machine. Then, all the components of torque, back-EMF, inductance, and unbalanced magnetic forces in the direction of the X-axis and Y-axis and their magnitudes are calculated. Afterward, the overload capability and the torque-speed characteristic are determined based on the average torque. Therefore, to maximize the torque/volume ratio, four metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Teaching Learn Base Optimization (TLBO), have been implemented, and the mentioned index is optimized. Since the said algorithms usually can minimize, its inverse is minimized instead of the index mentioned above being maximized. At this stage, the effect of three types of magnetization patterns, i.e., radial, parallel, and bar magnet in shifting, is also considered. The flux density of the permanent magnet changes concerning temperature. Finally, the effect of these changes on cogging, reluctance, and instantaneous torque, as well as back-EMF, unbalance magnetic force (UMF), torque-speed characteristic, and overload capability diagram, will be analyzed. The simulation was performed using MATLAB software.
Impact of glazing type, window-to-wall ratio, and orientation on building energy savings quality: A parametric analysis in Algerian climatic conditions
(Elsevier, 2024) Cherier, Mohamed Kamal; Hamdani, Maamar; Kamel, Ehsan; Guermoui, Mawloud; Bekkouche, Sidi Mohammed El Amine; Al-Saadi, Saleh; Djeffal, Rachid; Bashir, Maaz Osman; Elshekh, Ali. E. A.; Drozdová, Ľubomíra; Kanan, Mohammad; Flah, Aymen
Opaque surfaces, such as walls, are well-known for their significant contributions to heat loss and energy demands in buildings. However, transparent surfaces, such as windows, are equally critical to a building's energy performance. The design of these transparent elements requires a careful balance of various factors, including window size, glazing type, and orientation, each of which plays a pivotal role in enhancing energy efficiency. This study explores the optimization of these factors during the design process, emphasizing their impact on the overall building performance. This research evaluates the potential energy savings in a building archetype representative of the Algerian building stock. Utilizing the EnergyPlus simulation tool, the study conducted 1152 simulations on a baseline model to generate a comprehensive dataset detailing the building's energy demands for heating and cooling across various climatic conditions. The findings reveal that annual energy savings for this type of housing essentially depend on its climatic zone and can range from 6.92 % for a hot semi-arid climate (Bsh) to reach a maximum of 9.75 % in a cold semiarid climate (Bsk), a window-to-wall ratio (WWR) of 60 % typically maximizes energy efficiency, low-E glazing proved most effective in most cases, although regions needing significant solar protection favored alternative glazing types. Optimal window orientation generally trends Eastward, except in regions where southern exposure better supports solar management, highlighting the complex relationship between architectural design choices and energy efficiency.
Assessing the feasibility and quality performance of a renewable Energy-Based hybrid microgrid for electrification of remote communities
(Elsevier, 2024) Islam, Md Ashraful; Ali, M. M. Naushad; Mollick, Tajrian; Islam, Amirul; Benitez, Ian B.; Habib, Sidahmed Sidi; Al Mansur, Ahmed; Lipu, Molla Shahadat Hossain; Flah, Aymen; Kanan, Mohammad
Access to reliable energy is crucial for development, yet many rural areas in southern Bangladesh suffer from electricity shortages, impeding essential services and hindering social and economic progress. This paper proposes integrating renewable energy-based microgrids to provide sustainable and reliable electricity, thereby improving living conditions and boosting economic growth. A detailed survey in Ruma, Bandarban, was conducted for load estimation. Simulation results for on-grid and off-grid microgrids are obtained using HOMER Pro and PVsyst software. The off-grid system includes 21.8 kW of PV, 15 kW of hydro, and 222 kWh of battery storage, while the on-grid system includes a 200 kW PV system and a 15 kW hydro turbine. The levelized cost of energy (LCOE) is 0.15 USD/kWh off-grid and 0.03 USD/kWh on-grid. The on-grid system shows economic sustainability with a 6.8-year break-even point, 13 % IRR, and 8.7 % ROI. Environmental analysis shows significant greenhouse gas reductions, with CO2 emissions decreasing from 227,778 kg/year to 199,016 kg/year. Additionally, a sensitivity analysis is conducted, which underscores the resilience of the proposed hybrid microgrid system to weather variations and cost fluctuations. This paper provides a comprehensive foundation for policymakers to consider renewable microgrids as a solution for rural electrification in southern Bangladesh, utilizing solar and hydropower resources.
Computational insights into spin-polarized density functional theory applied to actinide-based perovskites XBkO3 (X = Sr, Ra, Pb)
(Springer Nature, 2025) Didi, Youssef; Belhajji, Mounir; BAHHAR, Soufiane; Tahiri, Abdellah; Naji, Mohamed; Rjeb, Abdelilah; Zaini, Hatim G.; Flah, Aymen; Ghoneim, Sherif S. M.; Abou Sharaf, Ahmed B.; Hashim, Mofreh A.
The exploration of perovskite compounds incorporating actinide and divalent elements reveals remarkable characteristics. Focusing on PbBkO3, RaBkO3, and SrBkO3, these materials were studied using density functional theory (DFT) via the CASTEP code to analyze their electronic, optical, and mechanical properties. The results show semiconductor behavior, with respective band gaps of 1.320 eV for PbBkO3, 3.415 eV for RaBkO3, and 2.775 eV for SrBkO3. Additionally, the elastic constants Cij, bulk modulus B, elasticity modulus G, Young's modulus Y, and Poisson's ratio v were optimized, highlighting anisotropic behavior. The mechanical stability of the compounds meets Born's criteria, and RaBkO3 stands out with a stable lattice dynamic, as demonstrated by phonon dispersion curves in the Pm-3 m space group. The optical properties of these materials indicate they are excellent absorbers of incident radiation, suggesting their potential for applications in magnetic sensors due to their anisotropic magnetic behavior, as well as for capturing solar radiation in the ultraviolet range.

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