Hasil untuk "Applications of electric power"

Xiaopeng Yuan, Haoyu Wang, Lei Zhang

This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems.

Juan Pablo Bertucci, Theo Hofman, Mauro Salazar

Electrification of marine transport is a promising solution to reduce sector greenhouse gas emissions and operational costs. However, the large upfront cost of electric vessels and the required charging infrastructure can be a barrier to the development of this technology. Optimization algorithms that jointly design the charging infrastructure and the operation of electric vessels can help to reduce these costs and make these projects viable. In this paper, we present a mixed-integer linear programming optimization framework that jointly schedules ferry operations, charging infrastructure and ship battery size. We analyze our algorithms with the case of the China Zorrilla, the largest electric ferry in the world, which will operate between Buenos Aires and Colonia del Sacramento in 2025. We find that the joint system and operations design can reduce the total costs by 7.8\% compared to a scenario with fixed power limits and no port energy management system.

Jiayi Guo, Kaizhuang Zhu, Xiaopeng Li et al.

Abstract To address the subjective issue of selecting measurement points based on mainstream line methods for hotspot temperature inversion in oil‐immersed power equipment, this paper demonstrates an oil‐immersed reactor temperature field inversion method based on random forest (RF) measurement point optimisation. Firstly, a temperature field calculation method for a 22‐kV oil‐immersed reactor is proposed. In combination with Latin hypercube sampling, 50 sets of temperature field data are calculated. Based on these samples, the selection of measurement points based on RF feature importance and the training of the genetic algorithm‐optimised back propagation (GA‐BP) inversion model are undertaken. Finally, the optimal combination of external tank wall measurement points is determined based on comprehensive error indicators, achieving accurate inversion of internal hotspot temperatures in the reactor (with an error of 0.243 °C). The inversion errors are reduced by 2.91 °C and 1.47 °C on average per group compared to existing methods, evincing the superiority of the proposed model.

Hadjira BELAIDI, Anis ZARA, Yasmine SAIDI et al.

This work focuses on developing an advanced robotic hand to assist individuals who have lost a hand. This robotic hand is designed to replicate the functionality and dexterity of a natural hand. Key features include wireless connectivity enabled by the ESP32 module, intuitive control through electromyography (EMG) sensors, and voice recognition capabilities. An accompanying Android application gathers health-related data and provides real-time notifications to healthcare providers.

Emili Salvador, Rosana Rodriguez, Enrique Miranda

Modeling in an emerging technology like RRAM devices is one of the pivotal concerns for its development. In the current bibliography, most of the models face difficulties in implementing or simulating unconventional scenarios, particularly when dealing with complex input signals. In addition, circuit simulators like Spice require long running times for high-resolution results because of their internal mathematical implementation. In this work, a fast, simple, robust, and versatile model for RRAM devices built in MATLAB is presented. The proposed model is a recursive and discretized version of the dynamic memdiode model (DMM) for bipolar-type resistive switching devices originally implemented in LTspice. The DMM model basically consists of two coupled equations: one for the current (non-linear current generator) and a second one for the memory state of the device (time-dependent differential equation). This work presents an easy-to-use tool for researchers to reproduce the experimental behavior of their devices and predict the outcome from non-trivial experiments. Three study cases are reported, aimed at capturing different phenomenologies: a frequency effect study, a cycle-to-cycle variability fit, and a stochastic resonance impact analysis.

Yasser Damine, Ahmed Chaouki Megherbi, Salim Sbaa et al.

The detection of faults in induction machines (IMs) is crucial for maintaining their optimal performance and extending their lifespan. Bearing faults, in particular, can have a significant impact on the efficiency and reliability of these machines. Ensemble Empirical Mode Decomposition (EEMD) is an appropriate technique for monitoring bearing health in IMs. This work is to evaluate the effectiveness of EEMD. The aim is to see in which level this technique can enhance the efficiency of bearing fault diagnosis. Our experimental findings indicate that EEMD exhibits greater effectiveness than VMD.

WU Renbo, HUANG Yijun

ObjectivesAs the proportion of renewable energy in power grids increases year by year, the volatility and uncertainty of the grid are significantly heightened, posing challenges to the safe operation of distribution networks. To address the issue of distributed network reconfiguration in high-proportion renewable energy grids, this paper proposed an online rolling optimization framework.MethodsThe framework utilized a distributed consensus protocol to obtain network topology and node operation information. It can enable automatic reconfiguration in the event of N-1 and N-2 line failures, allowing the distribution network to automatically restore normal operation without the need for additional external triggering signals, thus ensuring economic operation of the grid. Additionally, a rolling optimization method was employed to handle grid fluctuations caused by the high proportion of renewable energy, and generative adversarial network (GAN) technology was used to generate new data, which combined with historical data. It can help to achieve high-precision forecasting of grid operation data.ResultsThe proposed method can achieve automatic economic optimization and self-healing in normal, single-point failure, and two-point failure scenarios.ConclusionsThis method provides an effective solution for ensuring the safe operation of distributed networks in high-proportion renewable energy grids.

Sachinth Viththarachchige, Demy Alexander, Sarangan Rajendran et al.

With the increasing frequency of high impact low probability events, electricity markets are experiencing significant price spikes more often. This paper proposes a novel social equity driven optimal power flow framework to mitigate the adverse effects of price events that lead to such price spikes. The framework integrates social welfare optimization with socioeconomic considerations by including a socioeconomic score that quantifies the energy burden and socioeconomic status of consumers. By incorporating both supply cost and consumer satisfaction, the model aims to achieve a balanced and fair distribution of resources during price events, while considering resource scarcity and possible load curtailment. The proposed framework is tested for convergence on modified versions of the PJM 5-bus system and IEEE 24-bus reliability test system, discussing its potential effectiveness in enhancing social equity and optimizing power flow under system security constraints. Sensitivity analysis further highlights the impact of socioeconomic score on social welfare, providing insights for future improvements.

Oleh Pylypchuk, Oleh Strelko, Yuliia Berdnychenko

We are delighted to welcome you to the new issue of the journal on the history of science and technology! This issue is unique as it explores diverse aspects of the development of science and technology in various countries and historical periods. We invite you on an exciting journey through the pages of this issue, where you will find works by distinguished scientists such as Maryna Gutnyk, Florian Nürnberger, Tetiana Karmadonova, Natalya Pasichnyk, Renat Rizhniak, Нanna Deforzh, Liudmyla Zhuravlova, and many others. Their research covers various facets of history and technology. The collaborative work by Maryna Gutnyk and Florian Nürnberger presents a comprehensive exploration of the evolution of the Fe-C diagram, tracing its historical development through the lenses of various scientific contributions over time. Their analysis underscores the rich history behind this diagram, highlighting the foundational studies dating back to the early 19th century, marking crucial milestones in understanding the carbon content in steel and its implications for industrial applications. The authors' meticulous use of comparative analysis, synthesis, and chronological examination sheds light on the gradual refinement and evolution of the Fe-C diagram. From the initial recognition of graphite as pure carbon to the establishment of phase diagrams through collaborative efforts at international congresses, the Fe-C diagram's progression intertwines with the advancements of the industrial revolution. Tetiana Karmadonova's work on the migration trends of Ukrainian researchers from 1991 to 2023 provides a comprehensive analysis of the multifaceted factors driving the migration of scientists from Ukraine to various destination countries, particularly against the backdrop of recent events in the country. The study delves into the intricate landscape of migration among Ukrainian researchers across different historical periods. Natalya Pasichnyk, Renat Rizhniak, and Нanna Deforzh's meticulous study on the publications in the "Bulletin of Experimental Physics and Elementary Mathematics" from 1886 to 1917 offers invaluable insights into the organization, proceedings, and outcomes of domestic and international congresses of mathematicians and natural scientists during that period. Their research, focused on a comprehensive and quantitative analysis of these journal publications, sheds light on the pivotal role of these gatherings in the scientific and pedagogical realms Liudmyla Zhuravlova's research on the evolution of techno-nationalism and the pivotal role of space in this phenomenon from the 1980s to the 2020s offers a compelling exploration into the intricate dynamics of technological advancements and their influence on international relations and national strategies. The article delves deeply into the theoretical comprehension of techno-nationalism, particularly examining its relationship with space policy and its relevance within the context of US-China relations. Employing an interdisciplinary approach, drawing from historical, economic, political sciences, and international relations theory, the research unravels the dichotomous evolution of techno-nationalism juxtaposed against techno-globalism. Zhuravlova's work accentuates the ongoing power struggle between the US and China within the space industry, amplifying the techno-nationalist dimensions within innovation systems. Artemii Bernatskyi and Mykola Sokolovskyi's research presents a comprehensive review of the evolution of additive manufacturing (AM) processes within the realm of metallurgy, spanning from the foundational theories of layer-by-layer manufacturing to the contemporary landscape of AM technologies. This work illuminates the rapid advancements within the AM sector, capturing the profound interest of the scientific community. It underscores the dual significance of AM technologies - not only as an alternative manufacturing method for existing structures but also as a gateway to crafting new, intricately complex structures unattainable through traditional methodologies. Through meticulous analysis and classification of prior studies focusing on technological advancements and implementations, the research establishes a structured approach towards comprehensively mapping the development of additive manufacturing technologies in various trajectories. As a result, the research proposes a systematic approach to formulate a comprehensive scheme for AM technology development, thereby offering a framework that navigates the intricate landscape of technological advancements in various directions. Mykhailo Klymenko's meticulous study offers a comprehensive evaluation of Professor Tomasz Nikodem Ścibor-Rylski's pioneering contributions to the development of agricultural machinery testing during the latter half of the 19th century. This research sheds new light on Rylski's scientific endeavors and their significant impact on the evolution of agricultural equipment testing. Employing principles of historicism, scientific rigor, and objectivity, Klymenko utilizes historical-scientific methodologies, archival analysis, and generalization to present a nuanced understanding of Rylski's work. For the first time, archival documents are introduced, unveiling insights into the scientist's activities in advancing the field of agricultural machinery testing. Mohamad Khairul Anuar Mohd Rosli, Ahmad Kamal Ariffin Mohd Rus, and Suffian Mansor's insightful study delves into the overlooked yet pivotal role of electricity, specifically facilitated by the Perak River Hydro-Electric Power Company (PRHEPC), in the tin-mining industry within Kinta Valley during the period of 1927 to 1940. The research illuminates the historical emergence of electricity as a dominant power source in the tin-mining industry of Colonial Malaya, a topic that has received minimal attention in Malaysian historiography. Sana Simou, Khadija Baba, and Abderrahman Nounah's research represents a profound call to action amidst the urgent need to safeguard Morocco's cultural heritage, notably exemplified by the Marinid Madrasa within the Chellah archaeological site in Rabat. This research intricately weaves advanced technologies with a profound appreciation for the historical, social, and cultural significance of these sites. It charts a course that not only conserves architectural brilliance but also honors the profound stories encapsulated across epochs. Ultimately, it emerges as a blueprint for harmonizing the past with the present, ensuring the preservation of cultural heritage while embracing the imperatives of progress. In his article, Oleh Strelko shows that the history of bridge construction is an important part of historical knowledge. Developments in bridge construction technology reflect not only engineering advances, but also social, economic and cultural aspects of society. Engineers and scientists faced unique challenges when designing and building bridges depending on the technological level of the era, available materials and the needs of society. This process may reflect technological progress, changes in transportation needs, and cultural and social changes. The purpose of this article is to briefly review key moments and stages in the history of metal bridge construction using welding technology in the 20th century. We invite you on this exciting journey with our authors exploring the history of science, technology, and cultural heritage. May this issue broaden your knowledge and inspire new research endeavors!

Maliha Mahazabeen, Ali Jafarian Abianeh, Shayan Ebrahimi et al.

This study aims to improve the performance of sustainable electric vehicle chargers in the face of unpblackictable/unpreventable disturbances. Over the past few years, Dual Active Bridge (DAB) DC-DC Converters are procuring substantial recognition for electric vehicle charging applications due to their superior characteristics such as higher power density, bidirectional mode of operation, and higher efficiency. Unexpected disturbances and fault scenarios at both source and load sides can deteriorate DAB converters’ performance. In this study, the performance of a single-phase shifted DAB converter is enhanced to achieve desiblack output current under several disturbance conditions for electric vehicle (EV) charging applications. A Reinforcement Learning (RL) based Deep Deterministic Policy Gradient (DDPG) algorithm is deployed to proactively tune control parameters when the DAB undergoes certain unexpected disturbances including short circuit faults at the source and battery sides. Results show that the RL-tuned PI controller improves the rate of current overshoot significantly compablack with the manually-tuned PI controller. The method and results are validated through simulations in MATLAB/Simulink environment.

Yang Cui, Han Zhu, Yijian Wang et al.

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.

Brynjar Sævarsson, Hjörtur Jóhannsson, Spyros Chatzivasileiadis

This paper introduces the first quantum computing framework for Stochastic Quantum Power Flow (SQPF) analysis in power systems. The proposed method leverages quantum states to encode power flow distributions, enabling the use of Quantum Monte Carlo (QMC) sampling to efficiently assess the probability of line overloads. Our approach significantly reduces the required sample size compared to traditional Monte Carlo methods, making it particularly suited for risk assessments in scenarios involving high uncertainty, such as renewable energy integration. We validate the method on two test systems, demonstrating the computational advantage of quantum algorithms in reducing sample complexity while maintaining accuracy. This work represents a foundational step toward scalable quantum power flow analysis, with potential applications in future power system operations and planning. The results show promising computational speedups, underscoring the potential of quantum computing in addressing the increasing uncertainty in modern power grids.

Mukesh Gautam, Mohammed Ben-Idris

Game theory-based approaches have recently gained traction in a wide range of applications, importantly in power and energy systems. With the onset of cooperation as a new perspective for solving power system problems, as well as the nature of power system problems, it is now necessary to seek appropriate game theory-based tools that permit the investigation and analysis of the behavior and relationships of various players in power system problems. In this context, this paper performs a literature review on coalitional game theory's most recent advancements and applications in power and energy systems. First, we provide a brief overview of the coalitional game theory's fundamental ideas, current theoretical advancements, and various solution concepts. Second, we examine the recent applications in power and energy systems. Finally, we explore the challenges, limitations, and future research possibilities with applications in power and energy systems in the hopes of furthering the literature by strengthening the applications of coalitional game theory in power and energy systems.

HUANG Zhi, LIU Dong, CHEN Guanhong et al.

With the rapid development of communication and control technology in power distribution system,it has gradually shown the typical characteristics of cyber-physical systems,which not only brings new opportunities for development,but also brings multiple types of risks in the information system to the power system. As a result,cyber-physical cascading failures in the power system have occurred frequently,leading to serious accidents. Therefore,analyzing and studying the evolution process and failure consequences of cyber-physical cascading failures and exploring the internal mechanism of cyber-physical cascading failures have important theoretical and practical significance. Based on the event-driven model,an architecture of power distribution cyber-physical system is proposed to analyze the interaction mechanism between physical and information systems,and a research framework for the evolution mechanism of cyber-physical cascading failures is proposed. Then,the importance of the internal nodes of the information system is studied,the total risk value of the system and the defense resources are comprehensively considered,and the relevant parameters such as the probabilities of the information nodes being successfully attacked are calculated and the correlation matrices are established. Finally,a simulation of the evolution mechanism of cyber-physical cascading failures is carried out on a case,which verifies the rationality of the proposed mechanism and its validity in the deduction of the failure process and the calculation of the consequences.

Filippo Pellitteri, Vincenzo Di Dio, Christian Puccio et al.

In this paper, a DC-DC converter with an innovative topology for automotive applications is proposed. The goal of the presented power converter is the electrical storage system management of an electric vehicle (EV). The presented converter is specifically compliant with a 400 V battery, which represents the high-voltage primary source of the system. This topology is also able to act as a bidirectional power converter, so that in this case, the output section is an active stage, which is able to provide power as, for example, in the case of a low-voltage battery or a supercapacitor. The proposed topology can behave either in step-down or in step-up mode, presenting in both cases a high gain between the input and output voltage. Simulation results concerning the proposed converter, demonstrating the early feasibility of the system, were obtained in a PowerSIM environment and are described in this paper.

Azam Khan, Noor Shad Gul, Noor Shad Gul et al.

The importance of electroceramics is well-recognized in applications of high energy storage density of dielectric ceramic capacitors. Despite the excellent properties, lead-free alternatives are highly desirous owing to their environmental friendliness for energy storage applications. Herein, we provide a facile synthesis of lead-free ferroelectric ceramic perovskite material demonstrating enhanced energy storage density. The ceramic material with a series of composition (1-z) (0.94Na0.5Bi0.5TiO3-0.06BaTiO3)-zNd0.33NbO3, denoted as NBT-BT-zNN, where, z = 0.00, 0.02, 0.04, 0.06, and 0.08 are synthesized by the conventional solid-state mix oxide route. Microphases, microstructures, and energy storage characteristics of the as-synthesized ceramic compositions were determined by advanced ceramic techniques. Powder X-ray diffraction analysis reveals pure single perovskite phases for z = 0 and 0.02, and secondary phases of Bi2Ti2O7 appeared for z = 0.04 and 0.08. Furthermore, scanning electron microscopy analysis demonstrates packed-shaped microstructures with a reduced grain size for these ceramic compositions. The coercive field (Ec) and remnant polarization (Pr) deduced from polarization vs. electric field hysteresis loops determined using an LCR meter demonstrate decreasing trends with the increasing z content for each composition. Consequently, the maximum energy storage density of 3.2 J/cm3, the recoverable stored energy of 2.01 J/cm3, and the efficiency of 62.5% were obtained for the z content of 2 mol% at an applied electric field of 250 kV/cm. This work demonstrates important development in ceramic perovskite for high power energy storage density and efficiency in dielectric capacitors in high-temperature environments. The aforementioned method makes it feasible to modify a binary ceramic composition into a ternary system with highly enhanced energy storage characteristics by incorporating rare earth metals with transition metal oxides in appropriate proportions.

Muhammad Zakir, Ammar Arshad, Hadeed Ahmed Sher et al.

Abstract This paper presents practical implementation of a fault detection, localisation, and categorisation (FDLC) method in PV‐fed DC‐microgrid (DCMG). The DCMG is implemented by utilising a group of two DC nanogrids (DCNG) that have power control mechanism (PCM). The FDLC uses a voltage calculating circuit comprising a single voltage sensor and diode network. Moreover, the architecture is based on six statements extracted from the investigation of line to line (L–L) and line to ground (L–G) faults at a DCNG of the cluster. The PCM in the proposed system utilises a power triggering circuit for effective power flow among the different units of the DCNG considering the load demands and the resource availability. Experimentation is carried out by creating L–L/L–G faults at different points in the DCMG. Detection, localisation, and classification of faults is performed by utilising the sensor's voltage and power of the individual DCNG. FDLC offers less computational burden, perform fast detection of fault, and is capable of distinguishing between the L–L/L–G faults and uniform irradiance and partial shading conditions in the PV‐array. The proposed FDLC technique and its six statements are verified through experimental results.

Baojun Ge, Jiacheng Zhang, Dajun Tao

Abstract The stator ventilation channel is the key component that affects the cooling capability of the axial‐radial hybrid ventilation motor (ARHVM). To study the influence of the stator ventilation channel on fluid flow and temperature distribution in the ARHVM, taking a 500 kW medium‐sized high‐voltage ARHVM as the research object, the global calculation model of the ARHVM, including the external cooler, was established. The flow characteristics and temperature distribution of the motor were obtained using finite element analysis, and the calculated results were validated through test data. In addition, in view of the vortex phenomenon that hinders fluid flow in the stator radial ventilation duct, this study proposes a new type of stator ventilation channel structure, which can improve the fluid flow pattern and effectively suppress the vortex phenomenon. Compared with the original linear ventilation channel, the temperature of the stator region can be decreased by 1–2.5℃ via the new stator ventilation channel.

M. Cacciato, G. Aiello, F. Gennaro et al.

The paper presents a straightforward modelling approach to compute the power loss distribution in GaN HEMT based three phase and three level (3L) active neutral point clamped (ANPC) inverters, for different pulse width modulated techniques. Conduction and switching losses averaged over each PWM switching period are analytically computed by starting from the operating conditions of the AC load and data of GaN power devices. The accuracy of the proposed analytical approach is evaluated through a circuit based power electronics simulation tool, applied to different carrier-based PWM strategies.

Piotr Kuwałek

Voltage fluctuations are common disturbances in power grids, therefore the effective and selective process of identification and localization of individual voltage fluctuations sources is necessary for the minimization of such disturbances. Selectivity in the process of identification and localization disturbing loads is possible by the use cascade of blocks: demodulation, decomposition and propagation assessment. The effectiveness of this approach is closely related to the used method of decomposition. The paper presents the problem of decomposition process for the selected method of selective identification and localization of voltage fluctuation sources, in which the algorithm of enhanced empirical wavelet transform (EEWT) is used as the decomposition method. The paper presents selected research results from the real power grid, for which the result of selected approach causes mistakes in the process of identification and localization of voltage fluctuations sources. The potential causes of such mistakes related to the decomposition process are discussed on the basis of obtained research results.