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 377 results

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.
Indoor airborne VOCs from water-based coatings: transfer dynamics and health implications
(MDPI, 2025) Růžičková, Jana, Jana; Raclavská, Helena; Kucbel, Marek; Kantor, Pavel; Švédová, Barbora; Slamová, Karolina
Volatile organic compounds (VOCs) emitted from indoor surface coatings can significantly impact indoor air quality and health. This study compared emissions from water-based polyurethane (PUR) and acrylate–polyurethane (ACR–PUR) coatings, identifying 94 VOCs across 16 chemical classes. Time-resolved concentrations were analysed via Principal Component Analysis (PCA), which revealed distinct temporal emission patterns and chemically coherent clusters. Aromatic hydrocarbons, alcohols, esters, and isocyanates dominated the emission profiles, with ACR–PUR releasing markedly higher concentrations of symptom-relevant compounds. Acute exposure was linked to toluene, styrene, phenol, and methyl butyl ketone (MBK), which decreased sharply within 60 days, while compounds such as 1,3-dioxolane, isopropylbenzene, and ethenyl acetate exhibited persistent emissions, suggesting increased chronic risk. Although total VOC levels remained below the German UBA “excellent” threshold (<200 µg/m3), neurotoxic and carcinogenic compounds remained detectable. The combination of PCA-based temporal insights with toxicological profiling and emission transfer dynamics offers a refined framework for indoor air risk assessment. These results underscore the need to complement total VOC indices with symptom-oriented, time-resolved screening protocols to better evaluate SBS risk in indoor environments using water-based coatings.
Overview of the use of additives in biomass torrefaction processes: Their impact on products and properties
(Elsevier, 2024) Šafář, Michal; Chen, Wei-Hsin; Raclavská, Helena; Juchelková, Dagmar; Prokopová, Nikola; Rachmadona, Nova; Khoo, Kuan Shiong
Over the past few years, considerable attention has been devoted to enhancing the torrefaction process, exploring diverse additives to improve either the process itself or the characteristics of torrefaction products. This review examines the recent advancements in torrefaction processes conducted by different research groups for these purposes. A critical evaluation involving the usage of liquid- and solid-based additives in the torrefaction process can have diverse effects depending on the specific condition implied during the torrefaction process (e.g., biomass feedstock, process conditions, and desired outcomes). Therefore, various testing and evaluation procedures should be performed to determine the optimal type and quantity of additives for a specific torrefaction application. The influence of various additives on the torrefied products of different torrefaction processes is summarized in this review. In particular, the additives are systematically categorized, and the effects of the additives on the properties of the respective torrefaction products are also discussed.
Electric vehicle charging technologies, infrastructure expansion, grid integration strategies, and their role in promoting sustainable e-mobility
(Elsevier, 2024) Singh, Arvind R.; Vishnuram, Pradeep; Alagarsamy, Sureshkumar; Bajaj, Mohit; Blažek, Vojtěch; Damaj, Issam; Rathore, Rajkumar Singh; Al-Wesabi, Fahd N.; Othman, Kamal M.
The transport sector is experiencing a notable transition towards sustainability, propelled by technological progress, innovative materials, and a dedication to environmental preservation. This study explicitly examines the incorporation of electric vehicles (EVs) into the power grid, with a particular emphasis on passenger automobiles. Our analysis emphasises the vital importance of updated transport infrastructure in decreasing greenhouse gas emissions and aiding carbon reduction efforts in electricity networks. The analysis uncovers that adopting electric vehicles offers significant advantages, including enhanced grid efficiency and decreased emissions. However, it also brings issues concerning the design and operation of power systems at both the transmission and distribution levels. Key players are crucial in tackling these difficulties to improve electric vehicle integration into the grid. The study determines the most effective ways for distributing and providing electric vehicle charging infrastructure, and investigates the efforts made to establish common standards in order to solve current challenges. This research contributes to the advancement of sustainable mobility and energy systems by conducting a thorough examination of the impact of electric vehicles on power systems and offering appropriate integration solutions.
Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network
(Elsevier, 2024) Anum, Hira; Hashmi, Muntazim Abbas; Shahid, Muhammad Umair; Munir, Hafiz Mudassir; Irfan, Muhammad; Veerendra, A. S.; Kanan, Mohammad; Flah, Aymen
Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi -feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi -feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi -feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.
Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm
(Springer Nature, 2024) Rajagopalan, Arul; Nagarajan, Karthik; Bajaj, Mohit; Uthayakumar, Sowmmiya; Prokop, Lukáš; Blažek, Vojtěch
The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 ct, 160.9827 ct and 128.2815 ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.
Improved robust model predictive control for PMSM using backstepping control and incorporating integral action with experimental validation
(Elsevier, 2024) Djouadi, Hafidh; Ouari, Kamel; Belkhier, Youcef; Lehouche, Hocine; Bajaj, Mohit; Blažek, Vojtěch
The DC motor is being rapidly replaced in the industry by the permanent magnet synchronous motor (PMSM), which has a number of benefits over it. Nonlinear equations are used to describe the dynamics of the PMSM. It is susceptible to unidentified external disturbances (load), and its properties change over time. These constraints make it more difficult to exercise control. To overcome the non-linearities and the aforementioned shortcomings, non-linear controls are necessary. This manuscript refers to the development of a sturdy high-caliber position tracking controller that incorporates integral action for PMSM. A predictive control law for the speed loop is established, combined with the backstepping control law for the inner loop. The overall strategy can be divided into two distinct elements. The initial stage involves the derivation of a reference electromagnetic torque computed through the generalized non-linear predictive control method. Subsequently, the controller law is formulated utilizing the robust backstepping control technique. One of the cardinal merits of this method lies in its exemption from the requirement of measuring and observing the external disturbances and parametric uncertainties. The efficacy of this cutting-edge control approach is rigorously evaluated in simulation with MATLAB/Simulink environment and experimentally using OPAL-RT, under diverse operating conditions. The findings demonstrate steadfast resilience amidst external disruptions and adjustments to parameters, while ensuring swift convergence, a testament to its robustness and reliability.
On mechanism of the synthesis of boron-doped graphitic carbon nitride
(Elsevier, 2024) Cvejn, Daniel; Starukh, Halyna; Koštejn, Martin; Peikertová, Pavlína; Praus, Petr
B-doped graphitic carbon nitrides (B-CNs) represent a promising class of materials that are potentially useful in a variety of applications. Their properties depend on the nature of the B-doping, which in turn is highly dependent on the particular chemical mechanism of B-doping formation. With this in mind, we present here the study of Bdoping achieved by the co-calcination of CN-precursor (cyanoguanidine) and B-dopant (boric acid). In this study, the structural theory of the B-CN materials produced from different ratios of CN precursor and Bdopant was derived. Our proposed structure is supported by elemental analysis, X-ray diffraction, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and nuclear magnetic resonance. Based on our results, heptazine carbon replacing tetravalent B- species near the =N+=C--N- structural unit is the dominant pattern of B doping in our co-calcined materials. The identification of the nature of the B-doping allowed us to infer the mechanism of its formation in the chemical reactions taking place during calcination.
Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability
(Springer Nature, 2024) Gopi, Pasala; Alluraiah, N. Chinna; Kumar, Pujari Harish; Bajaj, Mohit; Blažek, Vojtěch; Prokop, Lukáš
Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.
Iron-modified Cu/γ-alumina catalyst for the selective hydrogenolysis of glycerol
(Elsevier, 2024) Skuhrovcová, Lenka; Kolena, Jiří; Frolich, Karel; Kocík, Jaroslav; Mück, Jáchym; Gholami, Zahra
This study introduces a novel Cu-based catalyst for the selective hydrogenolysis of glycerol to 1,2-propanediol, synthesized by impregnating mesoporous gamma-alumina with Cu and Fe. Characterization was performed using various analytical methods, and tests were conducted in a tubular continuous reactor under specific conditions. Iron was found to have multiple modifying effects, influencing the modification of Cu clusters on the catalyst surface and the radial Cu concentration profile inside the particles. A low Fe/Cu ratio resulted in an almost eggshell Cu profile, whereas higher Fe levels produced a more uniform distribution. Interestingly, minor Fe additions led to larger Cu clusters, while higher amounts resulted in smaller clusters and decreased glycerol conversion. The effect of Fe on Cu clusters size, acid sites concentration, and Cu radial profile, as well as the influence of these parameters on the glycerol conversion and selectivity towards 1,2-PD are discussed in this study.

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