Hasil untuk "Electricity and magnetism"

Qingyu Wang, Zishi Yang, Wei Hu et al.

ABSTRACT Resin impregnated paper (RIP) converter transformer valve‐side bushings are the key equipment in converter stations. Local overheating of the RIP core not only reduces the transmission efficiency but also causes insulation failure of converter transformers. In this paper, a new heat dissipation structure is proposed to improve the temperature distribution homogeneity of the bushing using two‐phase closed thermosyphon (TPCT). A test model is developed to determine the optimal working fluid inventory. Then, the temperature distribution of a ± 400‐kV RIP converter transformer valve‐side bushing with an optimised heat dissipation structure is obtained using the coupled three‐dimensional electromagnetic‐fluid‐thermal numerical simulation method considering multiphase flow and phase change processes. The influence of the new structure on the electric field is analysed. The simulation result is verified by the temperature rise test. The results show that two‐phase closed thermosyphon can reduce the maximum temperature of the RIP valve‐side bushings and significantly improve the temperature distribution homogeneity.

Pengpeng Wang, Wei Huang, Hao Su et al.

ABSTRACT Power grid is an indispensable infrastructure in modern society, in order to ensure the normal operation of the grid, online non‐contact monitoring of high‐voltage lines is essential. In this work, a ‘capacitor–laser diode (LD)–capacitor’ structure, namely, laser diode in capacitors (LDIC), that can be used for non‐contact monitoring of high‐voltage (HV) line status by directly transferring the status information of the HV line to modulated laser pulses is proposed. The proposed LDIC can accurately extract the real‐time voltage changes on the HV line with an accuracy level of 0.959%. Because the LDIC is sensitive to high‐frequency electromagnetic field, the LDIC is successfully utilised to detect the external electromagnetic interference (EMI) to obtain the intensity and frequency of the external EMI. Additionally, the amplitude and frequency of the HV line vibration can be accurately monitored by using the LDIC. For the third‐order curve fitting of vibration amplitude, the average error is only 0.00867, and the average error of linear fitting of vibration frequency is as low as 0.00655. This work provides a novel approach for the online monitoring of the HV line status and a new supplement for the development of power grid technology.

Rui ZHU, Yiding OU, Xiaotian LI et al.

In order to fully utilize the clean, flexible and economic characteristics of distributed energy(DE), it is needed to efficiently manage its comprehensive and flexible regulation characteristics. However, directly embedding the virtual power plant(VPP) model into the main grid scheduling is prone to the risk of privacy leakage and great computational burden of different energy entities. Therefore, it is necessary to aggregate the multi-category flexibility resources of VPP to calculate their equivalent external characteristics. To this end, this article proposes a VPP flexibility characterization method based on vertex search, which aggregates various flexibility within the VPP into the output power of the VPP nodes, whose mathematical essence is the projection of a high-dimensional flexibility space to a low-dimensional space. Then, by introducing the extrapolation-based vertex search method, we achieve the approximation of the projection by maximizing the Euclidean distance between hyperplanes. Finally, based on the flexibility equivalence method, we propose an evaluation method for VPP flexibility resources to explain the main grid operation cost and optimal solution changes before and after the VPP access from a geometric perspective. The case study shows that the proposed flexibility aggregation model can effectively reduce the operating cost of the main grids, and the proposed VPP value assessment method based on external characteristic equivalence can effectively evaluate the value generated by various types of flexible equipment under small changes of flexibility ranges.

Luyang LI, Longxiang CHEN, Lei CHEN et al.

When participating in primary frequency regulation of electrical power systems, it is necessary for new energy to address the issue of energy sources. Configuring energy storage is an effective way. In practical application, the configuration of energy storage needs to consider the factors of physical characteristics, unit cost, and other constraints to comprehensively compare the technical economy. However, the energy storage system configuration schemes proposed in existing literature mainly analyze operational scenarios and fail to consider various engineering constraints and actual cost data of energy storage. Based on the actual cost data, this article fully considers the physical characteristics and engineering design constraints of the energy storage system to study the economy of distributed access to units within the station for assisting primary frequency regulation of new energy. The article analyzes the technical characteristics and design constraints of three types of energy storage systems, namely lithium batteries, flywheels, and supercapacitors. On this basis, three types of configuration methods for energy storage for primary frequency regulation are proposed, and cost analysis and technical economy comparison are conducted based on the configuration schemes.

XU Zhenshun, YUAN Xiaohan, HUANG Ziheng et al.

This study aims to classify and differentiate mucinous and serous cystic neoplasms of the pancreas using a multi-source feature classification model based on deep learning for preoperative auxiliary diagnosis. Deep learning features and radiomics features were extracted from segmented images using deep learning and radiomics technology, respectively. Clinical features were also evaluated and quantified. LASSO (least absolute shrinkage and selection operator) and cross-validation methods were applied to screen the features, and two multi-source feature models were constructed: the radiomics combined with deep learning (RAD_DL) model and the clinical feature combined with RAD_DL (Clinical_RAD_DL) model. Traditional radiomics (RAD) and deep learning (DL) models were used as controls. SVM (support vector machine), ADAboost (adaptive boosting), Random Forest, and Logistic were selected for classification. The Clinical_RAD_DL feature model shows the best classification performance, with the accuracy of 0.923 1, recall rate of 0.882 4, precision of 0.882 0, F1-score of 0.882 2, and AUC value of 0.912 6. The experimental results indicate that the multi-source feature classification model based on deep learning has good performance in classifying pancreatic serous cystic neoplasms and pancreatic mucinous cystic neoplasms, and can assist clinical accurate diagnosis and treatment.

Feng Wang, Wangqiang Shen, Yuan Shui et al.

Single-atom magnetism switching is a key technique towards the ultimate data storage density of computer hard disks and has been conceptually realized by leveraging the spin bistability of a magnetic atom under a scanning tunnelling microscope. However, it has rarely been applied to solid-state transistors, an advancement that would be highly desirable for enabling various applications. Here, we demonstrate realization of the electrically controlled Zeeman effect in Dy@C84 single-molecule transistors, thus revealing a transition in the magnetic moment from 3.8 μB to 5.1 μB for the ground-state GN at an electric field strength of 3-10 MV/cm. The consequent magnetoresistance significantly increases from 600% to 1100% at the resonant tunneling point. Density functional theory calculations further corroborate our realization of nonvolatile switching of single-atom magnetism, and the switching stability emanates from an energy barrier of 92 meV for atomic relaxation. These results highlight the potential of using endohedral metallofullerenes for high-temperature, high-stability, high-speed, and compact single-atom magnetic data storage.

Amjad Alqahtani, DaVonne Henry, Lubomír Havlíček et al.

Single-molecule magnets (SMMs) with chemically tailorable properties are potential building blocks for quantum computing, high-density magnetic memory, and spintronics.1 2 3,4 These applications require isolated or few molecules on substrates, but studies of SMMs have mainly focused on bulk crystals. Moreover, fabrication of SMM-based devices and electrical detection of the SMM magnetic state are still coveted milestones that have so far been achieved mainly for double-decker rare-earth phthalocyanines at temperatures below 1 K.5-8 Here we demonstrate electrical detection of magnetization switching for a modification of the archetypal SMM Mn12, up to 70 K, based on the supramolecular spin valve effect5 with graphene quantum dots9. Notably, the exchange interaction between the molecules and the graphene, as well as the dot-mediated intermolecular interaction, can be directly extracted from the electrical response, opening the way to an effective characterization of the quantum properties of different types of SMMs in a wide temperature range.

Ming-lun Sun, Yanliang Xu, Wenjing Zhang

In order to solve a series of problems such as electromagnetic loss, mechanical strength, rotor dynamics, and vacuum cooling induced by the high-power machine in flywheel energy storage system (FESS), a multiphysics coupling field of electricity, magnetism, stress, thermal and fluid is adopted to conduct a comprehensive analysis of a high-capacity FESS. Firstly, a structure of high-power cup winding permanent magnet synchronous machine (PMSM) for wind power frequency regulation is proposed in this article of which the electromagnetic characteristics are analyzed by the finite element method (FEM). Secondly, the temperature of the flywheel under vacuum environment is analyzed by establishing a fluid-solid coupling thermal model. After that, the mechanical strength of the machine is researched based on the theory of elasticity and thermal coupling, and the rotor dynamics is studied by modal analysis. Finally, an overall prototype of the FESS is fabricated and the performance of electromagnetic, stress and temperature are validated. The experimental results verify the relevant theories and simulation analysis, which can finally realize the reliable operation of the high-capacity FESS.

Qinghua Cao, Wenjun Chen, Ying Zhong et al.

Hydrogel, a material with outstanding biocompatibility and shape deformation ability, has recently become a hot topic for researchers studying innovative functional materials due to the growth of new biomedicine. Due to their stimulus responsiveness to external environments, hydrogels have progressively evolved into “smart” responsive (such as to pH, light, electricity, magnetism, temperature, and humidity) materials in recent years. The physical and chemical properties of hydrogels have been used to construct hydrogel micro-nano robots which have demonstrated significant promise for biomedical applications. The different responsive deformation mechanisms in hydrogels are initially discussed in this study; after which, a number of preparation techniques and a variety of structural designs are introduced. This study also highlights the most recent developments in hydrogel micro-nano robots’ biological applications, such as drug delivery, stem cell treatment, and cargo manipulation. On the basis of the hydrogel micro-nano robots’ current state of development, current difficulties and potential future growth paths are identified.

Endalamaw Dessie, Desta Gebeyehu, Fikadu Eshetu

Abstract The purpose of this study was to explore the relationship and prediction of motivation, conceptual understanding, and critical thinking on metacognition in introductory physics. A correlational research design with multiple regression analysis was used to analyze the data from 84 first-year pre-engineering students from two public universities in Ethiopia. The instruments used were the Physics Motivation Questionnaire II, the Electricity and Magnetism conceptual assessment, the critical thinking of electricity and magnetism, and the metacognitive awareness and regulation of electricity and magnetism. The results showed that metacognition was significantly and positively associated with all three predictor variables, with motivation being the most strongly related, followed by conceptual understanding and critical thinking. However, only motivation and conceptual understanding predicted metacognition in the regression model. These results highlight the specific and combined roles of motivation, conceptual understanding, and critical thinking on metacognition and emphasize the need to consider these factors in physics education and research.

Chao Li, Gengyao Li, Ying Xin et al.

High-temperature superconducting (HTS) magnets are promising in the application of high-intensity magnetic field. HTS flux pumps are devices that can charge closed HTS magnets without direct electrical contact. Simulation is an effective way to clarify the physical mechanism and provide further insight into the design of the device. In this work, we propose an accurate and efficient modeling methodology to simulate the transformer-rectifier HTS flux pump, which has considered electromagnetic and thermal coupling. The validity of the model has been verified by experimental results and theoretical calculations. The working characteristics of the HTS flux pump are investigated based on the proposed model, including DC bias component in the charging loop, the voltage recovery delay of the dynamic bridge and the temperature distribution in the dynamic bridge. The simulation results clearly depict working details of the device, in terms of electricity, magnetism and heat. The proposed model can serve as a powerful tool to design the HTS flux pump in practical applications.

E. Ivanova

Dapeng Xu, Yifan Zhang, Song Zhang et al.

Abstract Copper nanostructures with low preparation cost have become the research hotspot in the fields of optics, electricity, magnetism and so on because of the complementary or higher properties than bulk materials. A novel macroscopic dendritic copper nanoleaves with the length of 3–22 mm prepared by solid-state ionics method at 4 μA direct current electric field (DCEF) using fast ionic conductor RbCu4Cl3I2 films was reported. The surface-enhanced Raman scattering (SERS) performance of prepared copper nanoleaves was detected by crystal violet (CV) and rhodamine 6G (R6G) aqueous solution as analyte molecules. The results present that the leaf width of copper nanoleaves ranged from 2 μm to 5 μm and many regularly arranged nanoparticles with the diameter from 10 to 70 nm existed on the prepared copper nanoleaves, which lead to the nanoleaves have high surface roughness. The copper nanoleaves contain only Cu elements and the fractal dimensions of copper nanoleaves was 1.381 because of macroscopic dendritic structures. The prepared copper nanoleaves composed of pure Cu have high surface roughness and the surface of copper nanoleaves can form a great many of hot spots, so the limit of detection (LOD) for CV and R6G detected by copper nanoleaves were as low as 1 × 10−12 mol/L and 1 × 10−13 mol/L, respectively. The centimeter-scale copper nanoleaves with facile material synthesis, high surface roughness, simple detection procedure and low LOD of analyte molecules have strong application prospect in trace detection of harmful food additives.

Mukesh Gautam

In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

Wei LIANG, Linlin WU, Qiping LAI et al.

When the short circuit ratio of receiving end AC system is small, the fault disturbance of sending end AC system may cause commutation failure of the inverter and overvoltage at the LCC-HVDC sending end AC bus. This paper studies the mechanism and influencing factors of overvoltage at the point of common connection (PCC) of the wind farm caused by an AC fault at the HVDC sending end. Firstly, an electromagnetic transient model is established for UHVDC wind power transmission system. Secondly, the influence mechanism of the wind farm and HVDC dynamic characteristics on the transient voltage at the PCC of wind farm under different sending end faults is analyzed. Then, the factors affecting the transient voltage variation at the PCC of the wind farm are analyzed and verified by simulation. The results show that the variation of transient voltage at the PCC of the wind farm is mainly affected by the AC bus voltage of the rectifier station and the power response characteristics of the wind farm. In addition, it is found that the influence characteristics of wind farm capacity, wind speed, and other factors on the voltage at the PCC of wind farm are different under single change and different combinations.

Hao-Hao Peng, Xin-Li Sheng, Shi Pu et al.

In Wigner function approach with relaxation time approximation, we calculate electric and magnetic conductivities of a fermion system in the strong magnetic field. The linear response has been calculated to the perturbation of electromagnetic fields on the background constant magnetic field. The Wigner function is separated into an equilibrium part in the background magnetic field and an off-equilibrium part induced by perturbative fields. The analytical expression for the equilibrium part and the corresponding equilibrium conditions are given. For the off-equilibrium part, we obtain the kinetic equation at the leading order in $\hbar$ from the master equation of the Wigner function. When perturbative fields only depend on the proper time, the off-equilibrium part can be analytically solved from which the vector and axial vector currents are obtained. We obtain the longitudinal and transverse Ohm conductivities as well as Hall conductivity as the linear response of the vector current to the perturbative electric field. The behaviors of these conductivities as functions of the evolving time, relaxation time, particle mass, and strength of the background magnetic field are investigated both analytically and numerically.

Iulian Radu, Bertrand Schneider

Learning physics is often difficult for students because concepts such as electricity, magnetism and sound, cannot be seen with the naked eye. Emerging technologies such as Augmented Reality (AR) can transform education by making challenging concepts visible and accessible to novices. We present a Hololens-based augmented reality system where collaborators learn about the invisible electromagnetism phenomena involved in audio speakers, and we measure the benefits of AR technology through quantitative and qualitative methods. Specifically, we measure learning (knowledge gains and transfer) and collaborative knowledge exchange behaviors. Our results indicate that, while AR generally provides a novelty effect, specific educational AR visualizations can be both beneficial and detrimental to learning they helped students to learn spatial content and structural relationships, but hindered their understanding of kinesthetic content. Furthermore, AR facilitated learning in collaborations by providing representational common ground, which improved communication and peer teaching. We discuss these effects, as well as identify factors that have positive impact (e.g., co-located representations, easier access to resources, better grounding) or negative impact (e.g., tunnel vision, overlooking kinesthetic feedback) on student collaborative learning with augmented reality applications.

Aize Hao, X. Ning

This review provides a comprehensive overview of the recent advances in the various typical spinel ferrite-based thin films with controlled synthesis, their performances, applications in multifunctional material fields, fundamental scientific challenges, and beyond. Firstly, the crystal structures of spinel ferrite-based thin films are introduced. Secondly, recent progress in traditional synthesizing and novel methods for preparation of spinel ferrite-based films are highlighted. Thirdly, their magnetism, electricity, optics performances, and applications in advanced information technology, energy storage and conversion, and environmental conservation fields are also summarized and discussed in-depth. Some effective strategies for optimizing performances and further applications are summarized. Finally, the present review work ends with a short discussion concerning the challenges, opportunities, and future prospects of spinel ferrite-based thin films.