Short-circuit Research Articles

High-performance lithium metal anode enhanced by multifunctional film of PAN@AgNWs with antimicrobial activity

Lithium metal is expected to be the perfect anode material for the next generation of rechargeable batteries owing to its ultra-high theoretical specific capacity, low density and the lowest redox potential. However, the practical application of lithium metal batteries faces serious challenges, including uneven lithium deposition, negative electrode volume expansion, and short circuits brought by dendritic protrusions. Although the 3D structure of silver nanowires is effective in inhibiting lithium dendrite growth and lithium metal volume expansion, it still suffers from lithium inhomogeneity caused by uneven distribution of silver nanowires and complicated preparation. Therefore, PAN@AgNWs collector was prepared by a simple suction filtration method, and uniform dispersion of silver nanowires and uniform deposition of lithium were achieved. Lithium nucleation overpotential and lithium dendrite formation can be effectively reduced by PAN@AgNWs hosts due to the reduction of local current density by 3D collectors and the improved lithiophilicity of silver nanowires. With low voltage hysteresis, the symmetric battery constructed with its composite anode may cycle steadily for 4500 hours at 1 mA cm−2 and 1 mAh cm−2. At the same time, the Li-PAN@AgNWs||LFP full cell still possesses a reversible of 144.18 mAh g−1 after 500 cycles at 1C. Moreover, due to large specific surface area and high surface energy of 3D silver nanowires, it exhibits excellent antimicrobial properties, with an inactivation efficiency of 96.3% after contacting with E. coli for 2.5 h. This work shows the possibility of using PAN@AgNWs in a large scale for LMBs, and paves the way for development of high-performance antimicrobial materials.

Electrically Small Antenna for Sensing Application: A Review

Abstract: This study focuses on the electrically small antennas (ESAs) tailored for sensing applications. Recognizing the critical role of antenna size and performance in modern sensing systems, the research employs advanced simulation and optimization techniques to explore various configurations. Additionally, discussions on potential enhancements, such as meta material, Dielectric resonating antenna, meander line antenna, etc. integration and reconfigurable structures, provide insights for future developments in compact antenna technology for wireless sensing systems. Thus, in In this paper, a comprehensive overview of various types of electrically small antennas (ESAs) is presented, along with their significance in enhancing performance and their application in 5G and 6G communication systems.

Разработка и исследование имитационной модели мультикамерного разрядника для грозозащитного троса

Optical fiber overhead ground wire (OPGW) is an effective solution for laying mainline digital communications along high-voltage power lines. It allows you to use the existing infrastructure of the power grid and ensure the transfer of a large amount of data. The flow of lightning currents and short-circuit currents can cause a violation of the thermal resistance of the fiber and a deterioration of its performance. When the OPGW is insulated through an insulator shunted by a spark gap, during operation it is possible to change the distance in the discharge gap due to the displacement of the electrodes in the plane. Also, when the short circuit location is close, multiple overlaps will occur and as a result, short circuit current will flow through the OPGW. Thus, the article considers the possibility to use a device with a multielectrode system developed on the principle of a multichamber spark gap as a replacement of the spark gap to increase the reliability of the unit with an isolated OPGW suspension. Modeling of the device has been carried out in the ANSYS Maxwell software package, the mathematical apparatus of which is based on the use of Maxwell's differential equations and the finite element method, which allows performing numerical modeling with sufficiently high accuracy. A simulation model of a multichamber spark gap has been developed in the ANSYS Maxwell software package and calculations of electrical characteristics have been performed. For the electrode system of a multi-chamber arrester, an uneven distribution of the applied voltage across the spark discharge gaps of the chambers is determined. It is shown that when a voltage is applied to a spark gap, the highest electric field strength occurs between the first and second electrodes, and then a cascade of discharge gaps occurs, which provides the required low discharge voltages of the spark gap as a whole. The developed simplified simulation model of a multi-chamber arrester has shown all the necessary conditions for cascade imaging of the voltage distribution and the electric-field strength of various electrode options. Based on the research conducted electrodes geometry is identified for smart prototype development and ongoing multi-chamber arrester research.

Unraveling Paradoxical Effects of Large Current Density on Zn Deposition.

Aqueous zinc-based batteries (ZBs) have been widely investigated owing to their intrinsic safety, low cost, and simple assembly. However, the actual behavior of Zn deposition under large current density is still a severe issue associated with obscure mechanism interpretation of ZBs under high loading. Here, differing from the conventional understanding that short circuit is induced by dendrite penetrating under large current density (10-100mA cm-2), the separator permeation effect is unraveled to illustrate the paradox between smooth deposition and short lifespan. Generally, a dense plating morphology is achieved under large current density because of intensive nuclei and boosted plane growth. Nevertheless, in the scenes applying separators, the multiplied local current density derived from narrow separator channels leads to rapid Zn2+ exhaustion, converting the Zn deposition mode from nucleation control to concentration control, which eventually results in separator permeation and short circuit. This effect is validated in other aqueous metal anodes (Cu, Sn, Fe) and receives similar results. Based on the understanding, a micro-pore (150µm) sponge foam is proposed as separators for large-current anodes to provide broader Zn2+ path and mitigate the separator permeation effect. This work provides unique perspectives on coordinating fast-charging ability and anode stability of ZBs.

ЗАСОБИ РОЗРАХУНКУ ТА ВИБОРУ УСТАВОК ДИФЕРЕНЦІЙНО-ФАЗНОГО ЗАХИСТУ ПОВІТРЯНИХ ЛІНІЙ МІКРОПРОЦЕСОРНИХ ЗАХИСТІВ L60 В СКЛАДНИХ ЕЛЕКТРИЧНИХ МЕРЕЖАХ ДО 10 000 ВУЗЛІВ

The means for calculation and selection of phase-differential protection settings of overhead lines of L60 microprocessor protections of the General Electric company in complex electrical networks up to 10,000 nodes was developed. This software allows to select settings according to the possible types of mixed signals: forward-sequenced and reverse-sequenced, as well as additional criteria. The program makes it possible to check the sensitivity of the starting and disconnecting devices in the case of symmetrical and asymmetrical short circuits, in the case of maximum reverse power flows in various mixed starts. Ref. 3 fig. 5.

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability.

Multi-granularity short circuit fault diagnosis for tokamak power converters under small sample conditions and noise interference

The power supply system of a tokamak device is a crucial subsystem responsible for providing the magnetic field for plasma confinement. The timely application of fault diagnosis techniques to identify abnormal states of the power supply is of crucial importance for the safe and efficient operation of tokamak devices. Intelligent diagnostic methods based on Convolutional Neural Networks (CNN) are widely employed nowadays. However, within the operational environment of the device, there exist multiple physical field couplings, and inevitable noise will impact the data. Moreover, the measurement equipment exhibits instability, resulting in limited availability of data that meets the required criteria. To address these issues, this paper investigates a fault diagnosis approach utilizing an Auxiliary Classifier Generative Adversarial Network (ACGAN) for data augmentation, and proposes a Multi-Granularity Denoising CNN (MGDCNN) that accounts for noise interference, enabling diagnosis of faults at different granularities. In our approach, we first construct a multi-granularity structure of the data based on prior knowledge. Then, a hierarchical structure corresponding to the multi-granularity data and robust to noise is built by introducing an embedded Discrete Wavelet Transform (DWT) into the one-dimensional (1-D) CNN. Finally, the natural stratification of the network is combined with individual granularity branches for fault diagnosis. Experimental results on simulated datasets and public benchmark datasets demonstrate that the proposed model achieves a diagnostic accuracy of 98.81 % for short-circuit faults in power supply converter at the finest granularity. Even with noisy data, the model maintains a diagnostic accuracy of approximately 97 %, indicating superior noise resistance performance in the inference process of the MGDCNN network.

Optoelectronic properties of Au/n-type Si semiconductor structures with SiO2 interlayer

In this study, we investigated the effect of light intensity on electrical and photovoltaic properties using current-voltage (I–V) measurements at room temperature of Au/n-type Si structures with SiO2 interface layer. At each light intensity, the obtained main electrical characteristics such as ideality factors (n), barrier heights (Φbo), rectification ratios (RR), saturation currents (I0) and series resistances (RS) of Au/SiO2/n-type Si structures were calculated from both thermionic emission (TE) and Cheung methods using I–V measurements and compared to each other. Experimental results showed that while the n and I0 values increased with the increase in the light intensity, the Φbo and RR values decreased. While the ideality factor values obtained using the TE method ranged between 1.973 (at dark) and 2.250 (for 100 mW/cm2), these values were obtained between 3.227 and 3.482 using the Cheung method. At the same time, while the barrier height values obtained using the TE method ranged between 0.865 eV (at dark) and 0.817 eV (for 100 mW/cm2), these values were obtained between 0.773 eV and 0.742 eV using the Cheung method. It was observed that while the n values obtained from TE theory were smaller than the values obtained from the Cheung method, the Φbo values were larger than the values obtained from the Cheung function. Furthermore, for the Au/SiO2/n-type Si structures, the photovoltaic parameters such as short circuit current (Isc) and open circuit voltage (Voc) and photocurrent (Iph) were examined under dark, daylight and various light intensities (from 20 to 100 mW/cm2).

A Method for Identifying External Short-Circuit Faults in Power Transformers Based on Support Vector Machines

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%.

Performance Evaluation of Grounding Systems of Medium-Voltage Concrete Poles: A Comprehensive Analysis

Designing and installing efficient grounding systems in power distribution networks is considered a complex and crucial task to ensure the reliable operation of power-protective schemes while mitigating hazardous potentials arising from faults, thereby safeguarding both personnel and equipment. This paper aims to offer guidance on designing effective grounding systems in distribution networks by assessing the influence of parameters such as soil structure, fault current magnitude, and fault clearing time. This involves proposing a more precise methodology for calculating hazardous potentials, leveraging software tools like PowerFactory, to accurately determine short-circuit (SC) currents and fault clearing times at specific locations where grounding grids are to be installed. Consequently, Distribution System Operators (DSOs) can design tailored grounding systems that optimize techno-economic considerations without unnecessary over-dimensioning, accounting for the unique characteristics of the Medium-Voltage (MV) Line and soil structure.

Protection Coordination Strategy for the Distributed Electric Aircraft Propulsion Systems

The current trend in distributed electric aircraft propulsion systems is to utilize the DC bus system at higher voltage levels than conventional aircraft systems. With Boeing and Airbus utilizing the +/−270 V bipolar DC bus system, the research on high-voltage systems is increasing gradually, with voltage levels ranging from 1 to 10 kV systems or +/−0.5 to +/−5 kV DC bus systems. These voltage levels present considerable challenges to the distributed electric aircraft propulsion systems. In addition to partial discharge effects, there are other challenges, particularly the challenge associated with effectively limiting short-circuit fault currents due to the low cable impedance of the distribution system. The cable impedance is a significant factor that determines the fault current during fault conditions. Due to the low impedance, there is a sharp increase in fault current, necessitating an enhanced protection strategy, which ensures that the system is adequately protected. This paper introduces a coordinated protection strategy specifically designed for distributed electric aircraft propulsion systems to mitigate or prevent short-circuit faults. The proposed algorithm utilizes an I2t-based strategy and the current-limiting-based strategy to protect the system from short-circuit faults and overload conditions. Redundant backup protection is also included in the algorithm in case the circuit breaker fails to operate.

Photovoltaic Effect of La and Mn Co-Doped BiFeO3 Heterostructure with Charge Transport Layers.

Bismuth ferrite BiFeO3 (BFO)-based ferroelectrics have great potential as inorganic perovskite-like oxides for future solar cells applications due to their unique physical properties. In this work, La and Mn co-doped BFO thin films with compositions Bi0.9La0.1(Fe1-xMnx)O3 (x = 0, 0.05, 0.1, 0.15) (denoted as BLF, BLFM5, BLFM10, BLFM15, respectively) were prepared via a sol-gel technique on indium tin oxide (ITO) glass. All the films are monophasic, showing good crystallinity. The optical bandgap Eg was found to decrease monotonously with an increase in the Mn doping amount. Compared with other compositions, the BLFM5 sample exhibits a better crystallinity and less oxygen vacancies as indicated by XRD and XPS measurements, thereby achieving a better J-V performance. Based on BLFM5 as the light absorbing layer, the ITO/ZnO/BLFM5/Pt and ITO/ZnO/BLFM5/NiO/Pt heterostructure devices were designed and characterized. It was found that the introduction of the ZnO layer increases both the open circuit voltage (Voc) and the short circuit current density (Jsc) with Voc = 90.2 mV and Jsc = 6.90 μA/cm2 for the Pt/ BLFM5/ZnO/ITO device. However, the insertion of the NiO layer reduces both Voc and Jsc, which is attributed to the weakened built-in electric field at the NiO/BLFM5 interface.

Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions

Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.

Advanced Detection of Failed LEDs in a Short Circuit for Automotive Lighting Applications.

This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold values indicating faults caused by open circuits or LED short circuits. With this algorithm, detecting cases of a few LEDs short-circuited within a branch, particularly a single malfunctioning LED, is particularly challenging. In this work, two easily implementable algorithms are proposed to address this issue within the vehicle's control unit. One is based on a mathematical prediction model, while the other utilizes a neural network. The results obtained offer a 100% LED short-circuit fault detection rate in the majority of analyzed cases, representing a significant improvement over the conventional method, even in scenarios involving a single malfunctioning LED within a branch. Additionally, the neural network-based model can accurately predict the number of failed LEDs.

Influence of positive temperature coefficient and battery aging on external short circuit for 18,650-type battery

Influence of positive temperature coefficient and battery aging on external short circuit for 18,650-type battery

Integration of Artificial Intelligence for Enhanced Coordination of DOCR Protection in Distributed Generation Systems

Distributed generation (DG) is an approach that involves adding decentralized power generation within a distribution network. Distributed generation systems can reduce transmission losses, increase the reliability of energy supply, minimize carbon emissions, and enable the active participation of consumers in energy production. However, with the increase in distributed generation, electric power systems face new challenges in maintaining operational reliability and safety. Disruptions such as short circuits or overcurrent can occur in the system, and appropriate protective responses are required to protect the power grid from more significant damage. The addition of DG also causes the short circuit current to vary and results in system protection coordination having to be redone. Carrying out coordination will take a long time. This research uses modeling and simulation of a distributed generation system with various operating conditions and works adaptively according to changes in the system due to the addition of DG. The results obtained from the simulation are used in neural network training to study the relationship patterns between directional overcurrent relays (DOCR) parameters and system operating conditions. The backpropagation algorithm is used in the Artificial Neural Network (ANN) training process. The training process utilizes the maximum Short Circuit Current (ISC) input obtained through generation, fault location, and fault type. Time Dial Setting (TDS) and Ipickup values are used as ANN training targets. After testing, the results obtained are in accordance with the target data. The efficacy of this method is further demonstrated through ETAP simulations, which confirm that ANN is a suitable approach for modeling adaptive and optimal relay coordination systems.

Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

Functional metal-organic framework membranes with uniform unsaturated pyridine-nitrogen sites as separators for high-performance lithium metal batteries

Lithium metal batteries are a promising successor to lithium-ion batteries due to high specific capacity, low density, and low electrochemical reduction potential. However, inhomogeneous lithium deposition on the Li surface will result in the formation of lithium dendrites and eventually puncture the separator and cause a short circuit. Herein, a functional metal-organic framework membrane with uniform unsaturated pyridine-nitrogen sites is synthesized via coating UiO-67-BPY (2,2 -bipyridine- 5,5 -dicarboxylic acid, named as BPY) on a commercial polypropylene (PP) separator. By replacing the benzene ring with a pyridine ring, the functionalization and porosity of MOF is fully utilized while maintaining the excellent porosity and pore size of MOF. The Li-ion transference number (tLi+) with the functional separator is 0.69 and the ionic conductivity is improved by order of magnitude compared to a plain PP separator. The N atom of the pyridine ring on the UiO-67-BPY has a strong electrostatic interaction force, and a weak coordination bond (N–Li bonding lattice) is formed between N and Li, which leads to an increase in the concentration of free lithium ions and makes their distribution more uniform. This allows lithium ions to migrate more smoothly, which is conducive to ionic conductivity and hinders dendrite formation.

Achieving 24.6 % efficiency in 2D perovskite solar cells: Bandgap tuning and MXene contact optimization in (BDA)(MA)n−1PbnI3n+1 structures

In the midst of the growing emphasis on renewable energy sources, there is an urgent requirement for materials that are devoid of lead and non-toxic, aligning with the demands of the 21st century. In this regard, the perovskite solar cells present themselves as a compelling option, offering cost-effectiveness and non-toxic nature. Nevertheless, these 3D perovskite materials encounter stability challenges. To mitigate these issues, 2D DJ (Dion Jacobson) perovskite material-based solar cells are increasingly utilized in contemporary applications. In this work, (BDA)(MA)n−1PbnI3n+1-based 2D DJ perovskite solar cells (BDPSC) are investigated. Further, it is crucial to use proper contact with BDPSC in order to eliminate the pinhole effect. Therefore, in this study, MXene contacts have been employed with BDPSC to combine the benefits of both, resulting in enhanced stability, superior charge transport, and tunable energy levels without pinhole effects. Further, the bandgap of the BDA active layer is adjusted by varying the number of inorganic layers (n) from 1 to 6 within the (BDA)(MA)n−1PbnI3n+1. Firstly, the thickness of the BDA active layer along with different MXene are varied for all the possible compositions of the (BDA)(MA)n−1PbnI3n+1. The result shows that the optimized thickness for the BDA layer is 1 µm, and the work function value of the left/right contact MXene is 3.56/5.65 eV. Additionally, this work also investigates the influence of grading on the PV performance of BDPSC. The result shows that the reported BDPSC can deliver a maximum open circuit voltage (VOC) is 1.18 V, short circuit current density (JSC) is 24.58 mA/cm2, Fill Factor (FF) is 84.96 % and efficiency is 24.58 %.

A hydrogel-based moist-electric generator with superior energy output and environmental adaptability

Harvesting energy from ubiquitous moisture has emerged as a promising way to collect energy irrespective of location and time. However, the electrical output of moist-electric generators (MEGs) is currently insufficient for practical applications. In this study, a highly efficient electricity-generating film was constructed by combining silicon dioxide (SiO2) nanofibers, sodium alginate (SA) and reduced graphene oxide (rGO) matrix impregnated with calcium chloride. The resulting SA–SiO2–rGO (SSG) film demonstrates a strong moisture-adsorption ability of 1.6 g/g at 80 % RH and 30 °C, and a relatively high ionic conductivity of 1.46 S m-1. The SSG film's strong moisture-adsorption capacity allows rapid ion dissociation, and its excellent electrical conductivity enhances ion migration. Thus, a single centimeter-sized device generates an open-circuit voltage (Voc) of up to ∼0.6 V and a short-circuit current (Isc) of ∼2.24 mA (0.14 mA cm−2). This short-circuit current is more than 10 times those of most reported MEGs. Moreover, an SSG-film-based MEG maintains a voltage output of ∼0.6 V over rather wide ranges of temperature (0–40 °C) and relative humidity (20 %–80 %), demonstrating superior environmental adaptability. In summary, this study provides fresh insights into the design of high-performance composite materials for efficient moist-electric energy conversion.