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 406 results

Time-domain geoelectrical modeling and experimental validation of Ground Potential Rise in multilayer soil structures during fault events
(Wiley, 2026) Mbasso, Wulfran Fendzi; Harrison, Ambe; Dagal, Idriss; Mahmoud, Mohamed Metwally; Tsobze, Kenfack Saatong; Jangir, Pradeep; Shaikh, Muhammad Suhail; Smerat, Aseel
Accurate characterization of subsurface electrical behavior during high-energy fault events is critical for both geotechnical safety assessment and the protection of power infrastructure. This study presents a geophysically driven, time-domain modeling framework for Ground Potential Rise (GPR) in multilayer and anisotropic soils, integrating electromagnetic field theory with physics-informed arc resistance modeling. The methodology employs apparent resistivity profiling and soil impedance mapping, enabling high-resolution simulation of current density and voltage gradients under realistic subsurface conditions. A coupled numerical-experimental approach is implemented: finite-element simulations incorporating layered earth resistivity are calibrated against controlled fault injection tests using scaled grounding grids in stratified soil. The model achieves an average deviation of less than 4.7% from measured GPR and step/touch voltages, demonstrating strong predictive reliability. Results reveal that conventional steady-state and homogeneous soil assumptions can underestimate hazardous step voltages by up to 63% and misrepresent the spatial extent of GPR zones by more than a factor of two. Comparative analyses show that optimized grounding grids reduce surface current densities by over 90% compared to isolated systems, significantly enhancing compliance with safety thresholds. Beyond its immediate application to substation and renewable energy grounding, the framework offers a transferable geoelectrical tool for infrastructure risk mapping, lightning hazard assessment, and geotechnical site evaluations in complex soil environments.
Enhanced PID controller tuning for nonlinear continuous stirred-tank heaters using a modified Newton-Raphson optimizer with random opposition and Lévy-flight learning
(Springer Nature, 2025) Rizk-Allah, Rizk M.; Ekinci, Serdar; Jabari, Mostafa; Izci, Davut; Bajaj, Mohit; Blažek, Vojtěch; Rubanenko, Olena
Accurate temperature regulation in continuous stirred-tank heater (CSTH) systems is vital in chemical and thermal process industries, where deviations can cause energy inefficiencies, product quality degradation, or even safety hazards. However, CSTH systems pose a formidable control challenge due to inherent nonlinearities, parameter uncertainties, and susceptibility to external disturbances. Conventional proportional-integral-derivative (PID) tuning methods often struggle to handle these complexities, resulting in sluggish responses or instability. This study introduces a modified Newton-Raphson-based optimization (mNRBO), for optimal PID tuning tailored to nonlinear CSTH environments. The mNRBO framework integrates two key innovations: random opposition learning, to enhance population diversity and prevent premature convergence, and L & eacute;vy-flight-based guided learning, to improve global exploration and escape local optima. These mechanisms are systematically embedded into the Newton-Raphson-based optimizer (NRBO) to achieve a robust exploration-exploitation balance. A CSTH dynamic model is formulated using mass and energy conservation principles, and a multi-objective cost function evaluates rise time, settling time, overshoot, and steady-state error under realistic process constraints. Simulation studies compare mNRBO with NRBO, hippopotamus optimization, golden eagle optimizer, and slime mould algorithm. Results show that mNRBO achieves the lowest cost function value 53.29, smooth convergence with standard deviation 0.90, and superior closed-loop performance with rise time 62.05 s, settling time 206.88 s, overshoot 1.41%, and steady-state error 0.006%. These findings confirm that mNRBO delivers high-precision, disturbance-resilient control and is a promising solution for industrial thermal processes requiring reliability, efficiency, and precision.
A novel analytical methodology for estimating high-frequency lumped model inductances and series capacitance of transformer winding: an indirect measurement procedure
(Elsevier, 2026) Chaouche, Moustafa Sahnoune; Didi, Faouzi; Amara, Abderrazak; Houassine, Hamza; Yousof, Mohd Fairouz Mohd; Tazay, Ahmad F.; Flah, Aymen; Metwaly, Mohamed K.; Ghaly, Ramy N. R., Ramy N. R.; Ghoneim, Sherif S. M.
In this article, a new analytical method is introduced to effectively estimate the self-inductance, mutual inductances, and series capacitance of transformer windings. The approach uses FR data collected at the winding terminals with the neutral open test. It applies an analytical formula that converts the sum of the inverse squares of both short-circuit and open-circuit natural frequencies, derived from the FR curve, into a polynomial function. These formulas are based on a lumped, mutually coupled equivalent model of the winding, with relationships expressed as a polynomial function connected by a factor relating the inductances, generalized to an N-1 degree for the N-th section of the model. By solving this polynomial, all winding inductance values can be accurately estimated, enabling the determination of the series capacitance. Notably, this method relies solely on measurements of the FR curve, ground capacitance, and equivalent inductance, providing an indirect yet highly efficient way to determine all parameters of the lumped mutually coupled equivalent model. This technique has been rigorously validated through experimental frequency response measurements on two air-core insulated windings, producing remarkably precise results that demonstrate its effectiveness in the field of frequency modeling.
Experimental examination of thermohydraulic characteristics of a new vibrating rubber tube turbulator with multiple air bubble outlets inserted inside a double-pipe heat exchanger
(Elsevier, 2026) Afridi, Muhammad Idrees; Pourahmad, Saman; Maleki, Nemat Mashoofi; Tavousi, Ebrahim; Rahbari, Alireza; Adibi, Tohid; Sharifpur, Mohsen
This experimental study explores a new method for improving heat transfer in heat exchangers by utilizing bubble injection alongside electromagnetic vibration techniques. Instead of conventional bubble injection, a vibrating rubber tube with multiple air outlets is employed to introduce bubbles into the working fluid. This configuration ensures uniform bubble distribution along both axial and radial directions, while the rubber tube's continuous vibration disrupts the thermal boundary layer, promoting turbulence and further enhancing heat transfer. The effects of various parameters are investigated, including Reynolds numbers spanning from 1050 to 7370, bubble injection flow rates between 0.5 and 2 l/min, rubber tube diameters of 3-5 mm, and air outlet numbers ranging from 30 to 90. Results show that increasing the bubble flow rate and tube diameter enhances both heat transfer and the friction coefficient. In contrast, increasing the number of air outlets improves heat transfer while reducing the friction coefficient. A maximum TEF of 4.42 is achieved at a bubble flow rate of 1.5 l/ min, a tube diameter of 5 mm, and 90 air outlets. Under these conditions, the Nusselt number and friction coefficient are up to 10.43 and 13.1 times higher, respectively, compared to those of a plain tube.
Comprehensive experimental performance investigation of conducted electromagnetic interference in split-phase induction motors: Common-mode
(Sage Publications, 2026) Miloudi, Mohamed; Miloudi, Houcine; Ardjoun, Sid Ahmed El Mehdi; Elzein, I. M.; Mahmoud, Mohamed Metwally; Mbasso, Wulfran Fendzi; Hussein, Hany S.; Ewais, Ahmed M.
Motors in Adjustable Speed Drive (ASD) systems are the major sources of conducted Electromagnetic Interference (EMI), and they are mainly the Common-Mode (CM) currents and voltages. Compliance with Electromagnetic Compatibility (EMC) standards is of utmost importance when maintaining system reliability in the face of ever-stricter Electromagnetic Compatibility standards in the industrial sectors. This work presents the first systematic experimental evaluation of CM impedance in Split Phase Induction Motors (SPIMs) in a wide frequency range (100 Hz to 100 MHz). Unlike prior studies that were limited to either a differential-mode analysis or limited frequencies in the experiment, the study provides comprehensive CM impedance data of two different SPIM setups, explaining resonance and anti-resonance behaviors that have direct implications on EMC performance. It is experimentally proven that high impedance designed motors significantly reduce CM current transfer, thus reducing EMI emissions and enhance EMC compliance. Particularly, the impedance peak of SPIM (I) was 8k at 100 MHz that translated to a 45% decrease in CM current and -15 dB attenuation of conducted EMI compared to SPIM (II). The resonance and anti-resonance frequencies determined the influence of motor architecture on its susceptibility to EMI. As a result, the findings provide prescriptive design information to the optimization of SPIMs in applications, for example, industrial automation and electric vehicle platforms, where very high EMI mitigation levels are of crucial importance.
Design of novel exponential PDN controller via quadratic interpolation optimiser for nonlinear and unstable ball and beam system
(Wiley, 2026) Izci, Davut; Ekinci, Serdar; Çelik, Emre; Uyar, Murat; Bajaj, Mohit; Blažek, Vojtěch; Rubanenko, Olena
This study presents a novel exponential proportional-derivative controller with filter (exp-PDN) for stabilising the nonlinear and underactuated ball and beam system. Unlike conventional PID-based approaches, the proposed controller removes the integral term, resulting in faster transient responses and improved robustness. It incorporates nonlinear exponential shaping of both the error and its derivative, along with a filtered derivative path for enhanced noise handling. A custom multi-objective cost function, comprising the squared error, settling time, and percent overshoot, is proposed to evaluate control performance. The quadratic interpolation optimiser (QIO), a recently developed metaheuristic based on analytical interpolation, is employed to optimise the controller parameters. To validate its effectiveness, the exp-PDN controller is compared against five state-of-the-art metaheuristic algorithms: QIO, spider wasp optimiser, komodo mlipir algorithm, golden eagle optimiser, and slime mould algorithm. The QIO-optimised exp-PDN achieves the best performance, with the lowest cost value (0.3211), minimal overshoot (5.52%), fast rise time (0.97 s), and smallest steady-state error (4.1643 x 10- 4). Further comparisons with QIO-optimised phase-lead and PID-with-filter controllers demonstrate the superiority of the proposed method in both transient and steady-state behaviour. In summary, this work advances the control of nonlinear unstable systems by delivering a structurally simple yet highly responsive control architecture. The combination of dual-channel exponential shaping and efficient metaheuristic optimisation results in state-of-the-art closed-loop performance, highlighting the practical value of the proposed exp-PDN framework for real-world control applications.
Fractional analysis for multiple solutions of thermodynamic model of Casson fluid under hydrodynamic and non-hydrodynamic optimization
(Elsevier, 2026) Abro, Shahnila Yaseen; Souayeh, Basma; Flah, Aymen; Hamdi, Monia; Abro, Kashif Ali; Faizan, Muhammad
This study investigates the flow behavior of a non-Newtonian Casson fluid influenced by hydromagnetic and non-hydromagnetic effects over an oscillating plate, subject to combined gradients of temperature and mass concentration. The analysis is framed within the context of linear fractional differential equations incorporating the Caputo-Fabrizio fractional derivative with a non-singular kernel. A mathematical model is developed, employing a linear boundary condition to characterize the temperature distribution, mass concentration, and velocity profiles. The governing equations are first non-dimensionalized and then extended into their fractional forms. An analytical solution is obtained using integral transform techniques, specifically the Laplace transform with its inversion and the Fourier sine transform with inversion. The break down the data analysis process under rheological variation for temperature and concentration is explored through which generalization and comparison is investigated. The key findings are focused on the flow and heat transfer characteristics, examining the influence of key dimensionless parameters. Moreover, the comparison between fractional and classical approaches are found in excellent agreement.
Design and optimization of localized plasmon resonance sensing via square-slotted Ag-graphene-dielectric metasurfaces for dermatological cancer identification using machine learning
(Springer Nature, 2025) Alsharari, Meshari; Flah, Aymen; Aliqab, Khaled; Pergl, Ivo; Kumar, Abhinav; Armghan, Ammar
Skin cancer is a dangerous, life-threatening illness impacting countless individuals globally, requiring urgent awareness, prevention, and early detection. It is one of the most common forms of cancer, often caused by excessive sun exposure or tanning, and requires early detection for effective treatment. Early detection of skin cancer is achievable through advanced sensor designs that utilize graphene material. Graphene's exceptional properties make it extremely appropriate for creating sensitive, accurate, and non-invasive diagnostic tools to identify cancer at early stages. The integration of silver (Ag), graphene, and silicon dioxide (SiO2) materials forms a highly sensitive multilayer structure, significantly enhancing the surface plasmon resonance response, which enables precise detection of skin cancer biomarkers at extremely low concentrations. An exceptional sensitivity of 1050 nm/RIU is attained, enabling efficient skin cancer detection through advanced plasmonic biosensing technology. Optimizing the biosensor design by systematically varying key physical parameters-such as layer thicknesses, slot dimensions, and material configurations-significantly enhanced its sensitivity. The optimization is also achieved by using a Machine learning algorithm. The highest R2 value of 0.99 is achieved for this research. This strategic tuning of the structural and optical characteristics enabled more accurate detection capabilities, making the sensor highly effective for early skin cancer diagnosis through plasmonic resonance.
Shining the dynamics of the Economic Complexity Index on the European Union's climate change strategy: Evidence from the novel approach of MMQR
(Elsevier, 2026) Kömürcüoglu, Ömer Faruk; Kömürcüoglu, Elif Duygu; Koçak, Sinem; Çi̇l, Dilek; Karis, Çiğdem; Güven, Aykut Fatih; Bajaj, Mohit; Blažek, Vojtěch
For the European countries, the issue of combating climate change has become a matter of existence. Therefore, it is of extreme importance to present economic-based evidence for these countries' climate action. One emerging yet underexplored area is the environmental implications of the Economic Complexity Index (ECI), which reflects the knowledge intensity embedded in a country's production structure. Despite its relevance, studies examining the relationship between ECI and environmental degradation (ED) in the European context remain scarce. This paper aims to fill this gap by investigating the impact of ECI on ED between 1995 and 2021, focusing on the European Union countries recognized for their environmental sustainability efforts. For this purpose, the relationship between ECI and two of the pioneer indicators of ED-ecological footprint (EFP) and carbon emissions (CO2)-is assessed through two separate models. To address the dynamic and heterogeneous structure of the relationship, the novel Method of Moments Quantile Regression (MMQR) approach is employed. Empirical evidence suggests that ECI contributes to ED, with a stronger impact observed on CO2 emissions than on EFP. Another key finding is that higher levels of ED limit the negative environmental effects of ECI. However, the robustness of the findings is confirmed using the Driscoll-Kraay (D-K) standard error estimator and also, the symmetric causality test of Dumitrescu-Hurlin (D-H). As global leaders in environmental initiatives, EU countries must guarantee the availability and variety of green financing sources to expedite the transition to sustainable production methods in sectors impacting the ECI index via the European Investment Bank and the EU Innovation Fund. Policymakers can provide favorable tax incentives to industries that implement eco-friendly production methods to lower their expenses, thereby rewarding these industries and fostering acceptance of this strategy among sectors beyond this framework. Achieving higher ECI scores through the integration of renewable energy and green technologies is therefore essential for EU countries striving for a greener and more resilient future.
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.

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