Electric field and force characteristic of dust aerosol particles on the surface of high-voltage transmission line

High-voltage transmission lines play a crucial role in facilitating the utilization of renewable energy in regions prone to desertification. The accumulation of atmospheric particles on the surface of these lines can significantly impact corona discharge and wind-induced conductor displacement. Accurately quantifying the force exerted by particles adhering to conductor surfaces is essential for evaluating fouling conditions and making informed decisions. Therefore, this study investigates the changes in electric field intensity along branched conductors caused by various fouling layers and their resulting influence on the adhesion of dust particles. The findings indicate that as individual particle size increases, the field strength at the top of the particle gradually decreases and eventually stabilizes at approximately 49.22 k V/cm, which corresponds to a field strength approximately 1.96 times higher than that of an unpolluted transmission line. Furthermore,when particle spacing exceeds 15 times the particle size, the field strength around the transmission line gradually decreases and approaches the level observed on non-adhering surface. The electric field remains relatively stable. In a triangular arrangement of three particles, the maximum field strength at the tip of the fouling layer is approximately 1.44 times higher than that of double particles and 1.5 times higher compared to single particles. These results suggest that particles adhering to the transmission line have a greater affinity for adsorbing charged particles. Additionally, relevant numerical calculations demonstrate that in dry environments, the primary adhesion forces between particles and transmission lines follow an order of electrostatic force and van der Waals force. Specifically, at the minimum field strength, these forces are approximately74.73 times and 19.43 times stronger than the gravitational force acting on the particles.

The influence of pore characteristics on rock fragmentation mechanism by high-voltage electric pulse

High-voltage electric pulse(HVEP)is an innovative low-energy and high-efficiency technique.However,the underlying physics of the electrical breakdown within the rock,and the coupling mechanism between the various physical fields involved in HVEP still need to be further understood.In this study,we establish a 2D numerical model of multi-physical field coupling of the electrical breakdown of porous rock with randomly distributed pores to investigate the effect of pore characteristics(porosity,pore media composition)on the partial electrical breakdown of rock(i.e.the generation of a plasma channel inside the rock).Our findings indicate that the generation of a plasma channel is directionally selective and extends in the direction of a weak electrical breakdown intensity.As the porosity of the rock increases,so does the intensity of the electric field in the‘electrical damage’region—the greater the porosity,the greater the effectiveness of rock-breaking.As the fraction of pore fluid(S_(water)/S_(air))gradually declines,the generation time of the plasma channel decreases,and the efficacy of rock-breaking by HVEP increases.In addition,in this study,we conducted an indoor experiment utilizing an electric pulse drill to break down the rock in order to recreate the growth mode of the plasma channel in the rock.Moreover,the experimental results are consistent with the simulation results.In addition,the development of this type of partial electrical breakdown is confirmed to be related to electrode polarity and pore characteristics via the experiment of the symmetrical needle-needle electrode arrangement,which further demonstrates the mechanism of partial electrical breakdown.This research is significant for comprehending the process of electric impulse rock-breaking and gives theoretical guidance and technological support for advancing electric impulse drilling technology.

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (ECa) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (ECas) and deep (ECad) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for ECas versus ECad. Spatial patterns were observed for the ECas and ECad readings in both fields. The patterns observed for the ECad readings were more distinct than the ECas measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.

Conformal Human–Machine Integration Using Highly Bending‑Insensitive,Unpixelated,and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

According to statistic data,machinery faults contribute to largest proportion of High-voltage circuit breaker failures,and traditional maintenance methods exist some disadvantages for that issue.Therefore,based on the wavelet packet decomposition approach and support vector machines,a new diagnosis model is proposed for such fault diagnoses in this study.The vibration eigenvalue extraction is analyzed through wavelet packet decomposition,and a four-layer support vector machine is constituted as a fault classifier.The Gaussian radial basis function is employed as the kernel function for the classifier.The penalty parameter c and kernel parameterδof the support vector machine are vital for the diagnostic accuracy,and these parameters must be carefully predetermined.Thus,a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c andδfor the support vector machine in each layer are determined by the particle swarm algorithm.The validity of this fault diagnosis model is determined with a real dataset from the operation experiment.Moreover,comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented.The results indicate that the proposed fault diagnosis model yields better accuracy and e-ciency than these other models.

Removal of phenol by activated alumina bed in pulsed high-voltage electric field

A new process for removing the pollutants in aqueous solution-activated alumina bed in pulsed high-voltage electric field was investigated for the removal of phenol under different conditions. The experimental results indicated the increase in removal rate with increasing applied voltage, increasing pH value of the solution, aeration, and adding Fe^2＋. The removal rate of phenol could reach 72.1% when air aeration flow rate was 1200 ml/min, and 88.2% when 0.05 mmol/L Fe^2＋ was added into the solution under the conditions of applied voltage 25 kV, initial phenol concentration of 5 mg/L, and initial pH value 5.5. The addition of sodium carbonate reduced the phenol removal rate. In the pulsed high-voltage electric field, local discharge occurred at the surface of activated alumina, which promoted phenol degradation in the thin water film. At the same time, the space-time distribution of gas-liquid phases was more uniform and the contact areas of the activated species generated from the discharge and the pollutant molecules were much wider due to the effect of the activated alumina bed. The synthetical effects of the pulsed high-voltage electric field and the activated alumina particles accelerated phenol degradation.

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.

Effects of High-voltage Pulse Electric Field Treatment on the Structure Stability of Konjac Glucomannan

Structures of KGM treated in two high-voltage pulse electric fields were characterized by infrared spectroscopy,Raman spectroscopy,X-ray diffraction and so on.The results showed that intermolecular hydrogen bonding interactions of KGM were reduced after being treated with high-voltage pulse electric field,but there was no significant effect on its fiber chain form and thermal characteristics.Results of the study can provide a useful reference for further study on the structure and property of KGM,and especially can provide theoretical basis for the effect of physical field on the foodstuff deep processing related to KGM.

Analysis on electrical characteristics of high-voltage GaN-based light-emitting diodes

In order to investigate their electrical characteristics, high-voltage light-emitting-diodes （HV-LEDs） each contain- ing four cells in series are fabricated. The electrical parameters including varying voltage and parasitic effect are studied. It is shown that the ideality factors （IFs） of the HV-LEDs with different numbers of cells are 1.6, 3.4, 4.7, and 6.4. IF increases linearly with the number of cells increasing. Moreover, the performance of the HV-LED with failure cells is examined, The analysis indicates that the failure cell has a parallel resistance which induces the leakage of the failure cell. The series resistance of the failure cell is 76.8 Ω, while that of the normal cell is 21.3 Ω. The scanning electron microscope （SEM） image indicates that different metal layers do not contact well. It is hard to deposit the metal layers in the deep isolation trenches. The fabrication process of HV-LEDs needs to be optimized.

CUTTING REGULARITY AND DISCHARGE CHARACTERISTICS BY USING COMPOSITE COOLING LIQUID IN WIRE CUT ELECTRICAL DISCHARGE MACHINE WITH HIGH WIRE TRAVELING SPEED

The analysis of cutting regularity is provided through using and comparing two typical cooling liquids. It is proved that cutting regularity is greatly affected by cooling liquid＇s washing ability. Discharge characteristics and theoretic analysis between two electrodes are also discussed based on discharge waveform. By using composite cooling liquid which has strong washing ability, the efficiency in the first stable cutting phase has reached more than 200 mm^2/min, and the roughness of the surface has reached Ra〈0.8 μm after the fourth cutting with more than 50 mm^2/min average cutting efficiency. It is pointed out that cutting situation of the wire cut electrical discharge machine with high wire traveling speed （HSWEDM） is better than the wire cut electrical discharge machine with low wire traveling speed （LSWEDM） in the condition of improving the cooling liquid washing ability. The machining indices of HSWEDM will be increased remarkably by using the composite cooling liquid.

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

Feasibility of a New Ironless-stator Axial Flux Permanent Magnet Machine for Aircraft Electric Propulsion Application

With the development of aviation electrification,higher demands for electrical machines are put forward in aircraft electric propulsion systems.The aircraft electric propulsion requirements and propulsion motor features are analyzed in this paper.Comparing with conventional PM machines,ironless stator axial flux permanent magnet(AFPM)machine topologies with Litz wire windings allow designs with higher compactness,lightness and efficiency,which are suitable for high-frequency and high-power density applications.Based on the motor requirements and constraints of aircraft electric propulsion systems,this paper investigates a high-power 1 MW multi-stack ironless stator AFPM machine,which is composed of four 250kW modular motors by stacking in axial.The design guidelines and special attentions are presented,in term of electromagnetic,thermal,and mechanical performance for the high-frequency coils and Halbach-array PM rotor.Finally,an ironless stator AFPM motor is manufactured,tested and evaluated with the consideration of cost and processing cycle.The results show that the output power is up to 53.8kW with 95%efficiency at 9000r/min at this stage.The proposed ironless stator AFPM machine with oil immersed forced cooling proves to be a favorable candidate for application in electric aircraft as propulsion motors.

Multiobjective Electric Machine Optimization for Highest Reliability Demands

This article is about illustrating a workflow for incorporating reliability measures to typical electric machine design optimization scenarios.Such measures facilitate comparing designs not only for rated conditions,but also allow to analyze their performance in the presence of unevitable tolerances.Consequently,by additionally considering reliability or robustness as objectives compared to conventional optimization scenarios,designs featuring low parameter sensitiveness can be obtained.The analysis of the design’s reliability as part of solving optimization problems involves a significant increase in required numerical evaluations.To minimize the associated prolongation of the runtime,an approach featuring a design of experiments based reduction of required computations and a consequent surrogate modeling technique is presented here.After successful training,the metamodel can be applied for fast evaluating lots of different parameter combinations.A test problem is defined and analyzed.Based on the observed findings,the necessity of incorporating robustness evaluations to machine design optimization becomes evident.In addition,the derived models allow for studying the impact of any tolerance-affected parameter on the machine performance in detail.This facilitates further beneficial studies,as for instance the analysis of selected changes of tolerance levels rather than a general minimization of the respective ranges which usually is associated with high production cost.

Implementation of Machine-to-Machine Solutions to Improve Quality of Service for Electrical Energy Distribution Networks in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of a GENEKO modem that exploits its digital inputs together with a full coverage of certain required auxiliary services so as to generate proper detection signals whenever failure currents occur;which allows incorporating the latest failure detection technology into the system.

High Specific Power Electrical Machines: A System Perspective

There has been a growing need for high specific power electrical machines for a wide range of applications.These include hybrid/electric traction applications,and aerospace applications.A lot of work has been done to accomplish significantly higher specific power electrical machines especially for aerospace applications.Several machine topologies as well as thermal management schemes have been proposed.Even though there has been a few publications that provided an overview of high-speed and high specific power electrical machines[1-3],the goal of this paper is to provide a more comprehensive review of high specific power electrical machines with special focus on machines that have been built and tested and are considered the leading candidates defining the state-of-the art.Another key objective of this paper is to highlight the key“system-level”tradeoffs involved in pushing electrical machines to higher specific power.Focusing solely on the machine specific power can lead to a sub-optimal solution at the system-level.

Quantitative Comparison of Electromagnetic Performance of Electrical Machines for HEVs/EVs

In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensively reviewed in terms of basic features,merits and demerits,and compared for HEV/EV traction applications.Their latest developments are highlighted while their electromagnetic performance are quantitatively compared based on the same specification as the Prius 2010 interior PM(IPM)machine,including the torque/power-speed characteristics,power factor,efficiency map,and drive cycle based overall efficiency.It is found that PM-assisted synchronous reluctance machines are the most promising alternatives to IPM machines with lower cost and potentially higher overall efficiency.Although IMs are cheaper and have better overload capability,they exhibit lower efficiency and power factor.Other electrical machines,such as synchronous reluctance machines,wound field machines,as well as many other newly developed machines,are currently less attractive due to lower torque density and efficiency.

Hybrid Excited Permanent Magnet Machines for Electric and Hybrid Electric Vehicles

This paper reviews various hybrid excited(HE)machines from the perspective of location of PM and DC excitation,series/parallel connection of PM and DC excited magnetic fields,and 2D/3D magnetic fields,respectively.The advantages as well as drawbacks of each category are analyzed.Since an additional control degree,i.e.DC excitation,is introduced in the HE machine,the flux weakening control strategies are more complex.The flux weakening performance as well as efficiency are compared with different control strategies.Then,the potential to mitigate the risk of uncontrolled overvoltage fault at high speed operation is highlighted by controlling the field excitation.Since additional DC coils are usually required for HE machines compared with pure PM excitation,the spatial confliction inevitably results in electromagnetic performance reduction.Finally,the technique to integrate the field and armature windings with open-winding drive circuit is introduced,and novel HE machines without a DC coil are summarized.

Recent Developments of Modulation and Control for High-Power Current-Source-Converters Fed Electric Machine Systems

The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor and offer high fault-tolerant capability for short-circuit faults.Besides,they provide motor friendly waveforms and four-quadrant operation ability.Therefore,they are suitable for high-power applications of fans,pumps,compressors and wind power generation.The purpose of this paper is to comprehensively review recent developments of key technologies on modulation and control of high-power(HP)PWM-CSC fed electric machines systems,including reduction of low-order current harmonics,suppression of inductor–capacitor(LC)resonance,mitigation of common-mode voltage(CMV)and control of modular PWM-CSC fed systems.In particular,recent work on the overlapping effects during commutation,LC resonance suppression under fault-tolerant operation and collaboration of modular PMW-CSCs are described.Both theoretical analysis and some results in simulations and experiments are presented.Finally,a brief discussion regarding the future trend of the HP CSC fed electric machines systems is presented.

Numerical-Analytical Method for Magnetic Field Computation in Rotational Electric Machines

A numerical-analytical method is applied for the two-dimensional magnetic field computation in rotational electric machines in this paper. The analytical expressions for air gap magnetic field are derived. The pole pairs in the expressions are taken into account so that the solution region can be reduced within one periodic range. The numerical and analytical magnetic field equations are linked with equal vector magnetic potential boundary conditions. The magnetic field of a brushless permanent magnet machine is computed by the proposed method. The result is compared to that obtained by finite element method so as to validate the correction of the method.