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 371 results

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.
Electric vehicle charging by use of renewable energy technologies: A comprehensive and updated review
(Elsevier, 2024) Nazari, Mohammad Alhuyi; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav; Prabaharan, Natarajan
The majority of the vehicles in the world consuming fossil fuels that causes emissions of harmful greenhouse gases. In order to mitigate the emissions regarding the transport sector, Electric Vehicles (EVs) have attracted attentions. One of the main concerns in the development of EVs is supply of required power for the charging stations. Renewable energy systems would be clean and attractive options for supply of required power of charging stations. In recent years, several studies have investigated applications of renewable energy systems for charging stations of EV and analyzed different aspects of these technologies. This article reviews the research works on the design, optimization and performance investigation of charging stations coupled with renewable energy systems. Studies on EV charging systems powered by stand-alone or hybrid renewable energy systems are considered in the present article. According to the reviewed works, significant potential of renewable energy systems for supply of charging stations can be concluded. Different factors such as the applied technology and components, meteorological data of the installation site and operating condition influence the performance of these systems. Furthermore, it can be concluded that control design system can influence various aspects of EV charging technologies powered by renewable energy systems such as share of renewables in the grid -connected configurations and performance reliability. Challenges and opportunities associated with the development of these systems are provided in the current works and some recommendations are highlighted for the forthcoming studies and projects.
Experimental variable band hybrid current mode control for high power high frequency inverter in electro surgical applications
(IEEE, 2024) Mohsin Rafiq, Muhammad; Ullah, Nasim; Prokop, Lukáš; Mišák, Stanislav
Electrosurgical generators (ESGs) are vital during medical operations, providing high-frequency electrical currents for cutting tissue and coagulation in surgery. Maintaining precise control over output power is challenging due to variable tissue loads. Inconsistent regulation can lead to undesirable surgical outcomes. This paper addresses this challenge through a novel Variable band hybrid current mode control (VBHCMC) technique. The study explores the limitations of existing approaches, such as peak current mode control (PCMC), emphasizing the need for improving control methodologies. The proposed VBHCMC method ensures stable output power, addressing issues associated with PCMC. It dynamically adapts the hysteresis band for variable load impedances, enhancing stability. The significance of this approach lies in its ability to combine the benefits of peak and valley current mode controls while maintaining a nearly constant switching frequency, significantly reducing steady-state errors. Results demonstrate significant reduction in steady-state errors compared to conventional PCMC. The proposed controller provides an effective solution to challenges faced in regulating output power during surgical procedures, enhancing safety and precision. The results have been verified in the MATLAB/Simulink environment, Processor-in-Loop (PIL) simulation in PSIM and using hardware validation.
Hybrid optimal-FOPID based UPQC for reducing harmonics and compensate load power in renewable energy sources grid connected system
(PLOS, 2024) Devi, T. Anuradha; Rao, G. Srinivasa; Kumar, T. Anil; Goud, B. Srikanth; Reddy, Ch. Rami; Eutyche, Mbadjoun Wapet Daniel; Aymen, Flah; El-Bayedh, Claude Ziad; Kraiem, Habib; Blažek, Vojtěch
Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.
A comprehensive review of wind power integration and energy storage technologies for modern grid frequency regulation
(Elsevier, 2024) Ullah, Farhan; Zhang, Xuexia; Khan, Mansoor; Mastoi, Muhammad Shahid; Munir, Hafiz Mudassir; Flah, Aymen; Said, Yahia
Integrating wind power with energy storage technologies is crucial for frequency regulation in modern power systems, ensuring the reliable and cost-effective operation of power systems while promoting the widespread adoption of renewable energy sources. Power systems are changing rapidly, with increased renewable energy integration and evolving system architectures. These transformations bring forth challenges like low inertia and unpredictable behavior of generation and load components. As a result, frequency regulation (FR) becomes increasingly important to ensure grid stability. Energy Storage Systems (ESS) with their adaptable capabilities offer valuable solutions to enhance the adaptability and controllability of power systems, especially within wind farms. This research provides an updated analysis of critical frequency stability challenges, examines state-of-the-art control techniques, and investigates the barriers that hinder wind power integration. Moreover, it introduces emerging ESS technologies and explores their potential applications in supporting wind power integration. Furthermore, this paper offers suggestions and future research directions for scientists exploring the utilization of storage technologies in frequency regulation within power systems characterized by significant penetration of wind power.
Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm
(Elsevier, 2024) Alkuhayli, Abdulaziz; Dashtdar, Masoud; Flah, Aymen; El-Bayeh, Claude Ziad; Blažek, Vojtěch; Prokop, Lukáš
In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm ' s enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.
Numerical analysis on inlet position and orientation for enhanced thermal performance of a solar thermochemical reactor for two-step WS cycle for hydrogen production
(Springer Nature, 2024) Sharma, Jeet Prakash; Kumar, Ravinder; Ahmadi, Mohammad Hossein; Bekbolatova, Zhannat; Sarsenbayev, Yerlan; Najser, Jan; Blažek, Vojtěch; Prokop, Lukáš
This study presents the effect of inert gas flow inlet positioning and orientation on the conversion efficiency of the proposed solar thermochemical reactor for hydrogen production. The nitrogen gas was used as (i) a reducing agent, (ii) a cooling agent to control the porous matrix temperature and (iii) removing oxygen from the STCR chamber. The result of the study demonstrates that the highest average temperature yield of 1570 K occurred at an inlet position of 10 mm with a 75° inclination, while 1665 K was obtained at an inlet position of 10 mm with a 90° inclination. Additionally, a temperature of 1353 K was achieved at an inlet position of 12 mm with a 75° inclination, both radially (for 20 mm thickness) and axially (over 80 mm length) along the centerline of the STCR chamber (extending 125 mm in length). The optimized inlet positioning and orientation provided the improved design of the solar thermochemical reactor (STCR) chamber capable of achieving the uniform solar flux profile and high-temperature distribution in the porous media to successfully carry out the redox reactions and achieve high solar-to-fuel conversion efficiency.

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