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 386 results

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, 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.
Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems
(Springer Nature, 2024) Karthik, N.; Rajagopalan, Arul; Bajaj, Mohit; Medhi, Palash; Kanimozhi, R.; Blažek, Vojtěch; Prokop, Lukáš
Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 ct for cost and 337.28 kg for emissions in the first scenario, 98.203 ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.
Securing modern power systems: Implementing comprehensive strategies to enhance resilience and reliability against cyber-attacks
(Elsevier, 2024) Abdelkader, Sobhy; Amissah, Jeremiah; Kinga, Sammy; Mugerwa, Geofrey; Emmanuel, Ebinyu; Mansour, Diaa-Eldin A.; Bajaj, Mohit; Blažek, Vojtěch; Prokop, Lukáš
Recent technological advancements in the energy sector, such as the proliferation of electric vehicles, and smart power electronic devices, have substantially increased the demand for reliable and quality power supply. This surge in energy consumption has posed significant concerns for traditional power systems regarding the systems' resilience and reliability. To address these challenges, power system engineers and researchers have proposed the digitalization of power systems, resulting in remotely controlled and operated smart grids. However, the transition towards smart grids has introduced new vulnerabilities, specifically in the form of cyber-attacks. One notable example is the recent malicious attack on the Ukrainian power system, which left three distribution networks destroyed, causing losses and damage to thousands of customers. In an era marked by rapid technological advancement, the security of modern power infrastructure against malicious cyber-attackers has emerged as a paramount concern for power system operators. This paper presents a comprehensive examination of cybersecurity strategies aimed at strengthening the resilience and reliability of modern power systems. By thoroughly analyzing the various cyber-attacks and effective defence strategies, it is evident that cybersecurity plays a crucial role in maintaining a continuous power supply and reducing the impact of potential contingencies. The study further provides valuable perspectives on the changing landscape of cyber threats faced by power infrastructure by combining insights from advanced research and industry expertise. By combining conventional techniques and cutting-edge technologies, valuable recommendations are provided for improving the cybersecurity of the power system and protecting vital grid assets, such as substations. This paper serves as an essential resource for policymakers, industry practitioners, and researchers seeking to understand the complex relationship between cybersecurity and modern power systems.
Modeling of traffic at a road crossing and optimization of waiting time of the vehicles
(Elsevier, 2024) Dimri, Sushil Chandra; Indu, Richa; Bajaj, Mohit; Rathore, Rajkumar Singh; Blažek, Vojtěch; Dutta, Ashit Kumar, ASHIT KUMAR; Alsubai, Shtwai
Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i, where 1 <= i <= 4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1-4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing.
Performance and robustness analysis of V-Tiger PID controller for automatic voltage regulator
(Springer Nature, 2024) Gopi, Pasala; Reddy, S. Venkateswarlu; Bajaj, Mohit; Zaitsev, Ievgen; Prokop, Lukáš
This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching-Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications.
Chemical compounds in PM10 as a tool for source apportionment
(Elsevier, 2026) Raclavská, Helena; Pfeifer, Christoph; Růžičková, Jana; Kucbel, Marek; Juchelková, Dagmar; Švédová, Barbora; Hrbek, Jitka; Slamová, Karolina
Volatile chemical products (VCPs) represent an emerging and under-recognised source of semi-volatile organic compounds in urban air, contributing to the chemical complexity and secondary formation potential of PM10. Despite growing awareness of their role in atmospheric chemistry and exposure, real-world data on VCP-derived species in ambient particles remain scarce. This study provides the first integrated characterisation of VCP-related compounds in PM10 for Central Europe. PM10 samples were collected from & Uacute;st & iacute; nad Labem, Zdiby, M & ecaron;ln & iacute;k between November 2022 and April 2023 and analysed using TD-GC/MS. A total of 157 compounds were classified, 106 of which were uniquely associated with product emissions. VCP markers accounted for 0.59-2.11 % of all identified organics, equivalent to 0.05-0.43 mu g/m(3). Among conventional sources, traffic and biomass burning dominated over coal, while biogenic markers were regionally variable. Plasticisers were pervasive: phthalate esters (PAEs) and non-phthalate plasticisers (NPPs) occurred at most sites. Given EU restrictions on cosmetic PAEs, their ambient levels (Sigma PAE 18-54 ng/m(3)) mainly reflect polymer and plastic emissions rather than personal-care sources. Sigma NPP 6-14 ng/m(3) were ubiquitous but source-ambiguous; therefore, the Sigma NPP/Sigma PAE ratio is introduced as a new diagnostic indicator of phthalate substitution, revealing a clear regional gradient (& Uacute;st & iacute; 2.6 >M & ecaron;ln & iacute;k 1.1 >Zdiby 0.3). Fragrance-related terpenes showed stronger product than biogenic signatures, and significant fragrance-PAE correlation (r = 0.67) indicates functional coupling in emissions. Overall, concentrations were comparable to or below urban levels reported elsewhere, confirming that Central Europe is undergoing an early yet measurable chemical transition in PM10 composition driven by consumer-product and polymer-related emissions.
Using synthetic data for pretraining partial discharge detection in overhead transmission lines
(Springer Nature, 2025) Klein, Lukáš; Fulneček, Jan; Kabot, Ondřej; Dvorský, Jiří; Prokop, Lukáš
Accurate detection of partial discharges (PDs) in medium-voltage overhead transmission lines is critical for preemptive maintenance and avoiding costly outages, yet it is challenged by scarce labeled data and pervasive electromagnetic interference. This paper investigates a hybrid simulation-and-data-driven framework in which synthetically generated PD signals are used to pretrain deep neural networks and are subsequently fine-tuned on a limited set of real overhead-line measurements. The synthetic pipeline systematically varies PD repetition rates, amplitude distributions, vegetation-contact scenarios, and noise conditions, producing diverse time-series and spectrogram-like representations that approximate real operating environments. We conduct a comprehensive ablation study across multiple architectures—Convolutional Neural Networks (CNNs), a Vision Transformer (ViT), and a Long Short-Term Memory (LSTM) network—and analyze their sensitivity to granular sweeps of synthetic-data parameters. CNN-based models decisively outperform ViT and LSTM counterparts on the spectrogram-based classification task, while ViT and LSTM fail to learn meaningful representation. For the successful CNNs, pretraining on carefully parameterized synthetic datasets—particularly those reflecting higher PD activity, such as our Datasets 3 and 4—consistently improves downstream performance on real data, boosting the Matthews Correlation Coefficient (MCC) on imbalanced, cost-sensitive test sets by roughly 10–20% compared with training from scratch. At the same time, we show that poorly aligned synthetic data can degrade generalization, underscoring the need for accurate noise calibration and domain-aligned simulation. Overall, the results confirm that (i) architectural choice is pivotal for PD detection in overhead lines and (ii) well-designed synthetic data is a powerful, practical lever for achieving reliable and cost-effective PD monitoring when real labeled data are limited.
Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights
(Elsevier, 2025) Samanta, Indu Sekha; Mohanty, Sarthak; Parida, Shubhranshu Mohan; Rout, Pravat Kumar; Panda, Subhasis; Bajaj, Mohit; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav
Power Quality (PQ) disturbances are critical in modern power systems, significantly impacting electrical networks' stability, reliability, and efficiency. With the increasing penetration of renewable energy sources, non-linear loads, and power electronic devices, the detection, classification, and mitigation of PQ disturbances have become more complex. Traditional PQ analysis methods, which rely heavily on human expertise and rule-based systems, are often insufficient in handling the growing complexity and volume of data in real-time applications. This review comprehensively analyzes the latest advancements in Artificial Intelligence (AI) and Machine Learning (ML) techniques applied to PQ analysis, achieving classification accuracies as high as 99.94 % with hybrid approaches like dual-tree wavelet packet transforms combined with extreme learning machine (ELM). Integrating advanced signal processing techniques, such as wavelet transforms and empirical mode decomposition, has demonstrated accuracy improvements of up to 5 % in challenging scenarios. This paper explores the challenges associated with AI-based PQ analysis, including the need for large datasets, overfitting issues, and the lack of interpretability in complex models. Future research directions are outlined, emphasizing the development of hybrid models, explainable AI systems, and real-time adaptability to dynamic grid conditions. This review provides a holistic understanding of state-of-the-art AI/ML methods in PQ analysis. It highlights their potential to transform modern power systems by ensuring higher reliability, better fault detection, and more efficient power delivery.
Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning
(2024) Foong, Loke Kok; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav; Atamurotov, Farruh; Khalilpoor, Nima
This paper investigates the application of three nature-inspired optimisation algorithms - SHO, MFO, and GOA - combined with four machine learning methods - Gaussian Processes, Linear Regression, MLP, and Random Forest - to enhance carbon dioxide emission prediction in the OECD - Asia and Oceania region. The study uses historical carbon dioxide emissions data, socioeconomic indicators such as GDP, population density, energy consumption, and urbanisation rates, and environmental indicators such as temperature, precipitation, and forest cover. Through comprehensive experimentation, the study evaluates the performance of each combination, revealing varying effectiveness levels. The MFO-MLP combination achieved the highest accuracy with R-2 values of 0.9996 and 0.9995 and RMSE values of 11.7065 and 12.8890 for the training and testing datasets, respectively. The GOA-MLP configuration achieved R-2 values of 0.9994 and 0.99934 and RMSE values of 15.01306 and 14.59333. The SHO-MLP combination, while effective, showed lower performance with R-2 values of 0.9915 and 0.9946 and RMSE values of 55.4516 and 41.575. The findings suggest hybrid techniques can significantly enhance prediction accuracy compared to conventional methods. This research provides valuable insights for policymakers and stakeholders, indicating that optimised machine learning models can support more informed and effective environmental policy-making and sustainability efforts in the OECD - Asia and Oceania region. Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. These findings offer a robust tool for policymakers to forecast emissions more accurately, aiding in developing targeted strategies to reduce carbon footprints and achieve climate goals.
Priority-based scheduling in residential energy management systems integrated with renewable sources using adaptive Salp swarm algorithm
(Elsevier, 2024) Panda, Subhasis; Samanta, Indu Sekhar; Rout, Pravat Kumar; Sahu, Binod Kumar; Bajaj, Mohit; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav
With the remarkable growth and implementation of communication technology, sensors, and measurement equipment in the Smart Grid (SG) environment, demand side management (DSM) and demand response (DRs) can be easily implementable in residential energy systems integrated with renewable energy sources (RES). Looking at this perspective, this paper suggests an intelligent and dynamic load-priority-based scheduling optimal smart residential energy management system (REMS). The objectives to achieve through priority-based scheduling in the case of a residential energy management system are multi-focussed in terms of peak load reduction, consumer choice of consumption according to priority basis, and cost-effectiveness towards electricity price savings. The issues related to uncertainties with RES due to environmental dependency must be incorporated into the DSM. A single objective discrete formulation based on the Adaptive Salp Swarm Algorithm (ASSA) has been done on modelling and optimizing the crucial system parameters for scheduling, ideally the operation of residential appliances, along with the sources and prioritized-based loads available. System constraints, consumer priorities, energy source availability, uncertainties, and objectives are considered in the formulation to justify the approach that is feasible in real-time conditions. To enhance the search capabilities of SSA, the control parameters vary optimally in both the exploration and exploitation stages of searching. Comparative results with genetic algorithms (GA), particle swarm optimization (PSO), and conventional SSA are presented in different cases, such as (1) traditional homes without REMS, (ii) smart homes with REMS (iii) smart homes using REMS with RES.
Intelligent techniques for prediction characteristics of shell and tube heat exchangers: A comprehensive review
(Elsevier, 2024) Nazari, Mohammad Alhuyi; Ahmadi, Mohammad Hossein; Mukhtar, Azfarizal; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav
Heat exchangers are widely used in different chemical industries and energy systems. Among different types of heat exchangers, shell and tube heat exchangers are among the most conventional ones that have significant share in the market and industry. Performance of shell and tube heat exchangers is affected by a variety of factors which can lead to some difficulties and complications in the modeling by use of numerical simulation. Intelligent techniques like artificial neural networks would be practical solution for modeling and simulation of these heat exchangers with significant exactness. In this regard, scholars have applied these methods for performance prediction and modeling characteristics of shell and tube heat exchangers in recent years. In the present article, studies on the modeling of different characteristics of shell and tube heat exchangers such as Nusselt number, pressure loss and fouling are reviewed and their key findings are represented. The findings of the study revealed that employment of proper intelligent methods can lead to exact performance prediction of these devices with R2 values of as high as 0.99 for both heat transfer coefficient and pressure drop. Moreover, it is reported in the reviewed studies that performance of these approaches is influenced by a variety of factors such as the applied techniques in the model and their structure. The developed model by the intelligent techniques for would be applicable for performance prediction, design and optimization of shell and tube heat exchangers. Finally, some recommendations are provided for the future studies that would be helpful in development of more precise and comprehensive models.

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