Electrical Circuit Research Articles

Modeling and Comparison Study of Industrial AC-Arcs

Electric arcs are a necessary heat source in many industrial processes that take place in Submerged Arc Furnaces (SAFs). Arcs exhibit non-linear electrical characteristics and behave in a complex manner. Therefore, an improved understanding of their behavior enables better control of furnace operation. Modeling of industrial arcs is a multiphysics process that involves simultaneously solving several coupled physical phenomena, such as electromagnetics, fluid dynamics, and heat transfer, including a radiative heat transfer from the plasma arc. Coupling fluid dynamics and electromagnetics is known as Magnetohydrodynamics (MHD). For practical applications, however, there are also simpler approaches to arc modeling, either based on simplified physical principles or empirical behavior. In this paper, a combined Cassie–Mayr model (CMM) and a channel arc model (CAM) are implemented and coupled with a submerged arc furnace electrical circuit model. The complete circuit model parameters such as resistances and inductances are estimated using modeling of a full size furnace, and then, actual measurements from a SAF are used to validate the models by comparing current and voltage waveform. Both models are then used to estimate harmonic distortion in a SAF for different arc current ratios, which should help operators to estimate the arc current in real time thus be able to lower and raise the electrode to keep operating conditions constant.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

Intermittent Electrification with Battery Locomotives and the Post-Diesel Future of North American Freight Railroads

Changing attitudes, regulations, public policy, and international treaties concerning fossil fuels are likely to lead freight railroads toward carbon-neutral technologies, yet only electric traction matches the performance of diesel-electric locomotives. The tremendous power requirements of freight trains make efforts to reduce greenhouse gas (GHG) emissions challenging, but the prospect of 7.2 megawatt-hour battery-electric locomotives (BELs) offers promise, and intermittent electrification can facilitate battery charging. Train performance calculations under simulated real-world conditions show four 7.2 megawatt-hour BELs can power 8,000-ton trains up to 230 mainline miles unassisted, with average energy consumption of 12.5 watt-hours/ton-mile. This permits discontinuous electrification of major freight lines, leaving “gaps” of up to 200 mi to reduce capital costs, especially in rugged terrain or where the power grid is sparse. Massive onboard battery arrays and intermittent access to the electrical grid for traction power and recharging provide great energy savings by recycling energy now lost when traveling downhill or braking. A case study of a hypothetical Class I railroad found intermittent electrification with BELs more than 60% more cost-effective than contiguous electric districts, and dramatically reduced engine changes. To reduce railroad GHG emissions, governments must support technical development, show that electrification works in various North American settings, and develop institutional-financial frameworks to incentivize intermittent electrification with BELs, all in the context of massive environmental and capacity upgrades to electrical networks. Given proper assistance and incentives, early 21st century railroads may find discontinuous overhead-wire electrification offers great promise in operating terms.

Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems

This paper presents a mathematical optimization framework for the strategic placement of quasi-dynamic wireless charging (QWC) stations within road networks to address the charging needs of battery electric buses (BEBs). This study evaluates two scenarios for powering the buses. In the first scenario, a grid-connected system is considered. The optimization aims to minimize annual costs related to capital, operation, and energy losses of the electric bus fleet. This involves determining the optimal locations for QWC stations, the length of power transmitters, and the corresponding battery capacities for the BEBs. Using MATLAB-based optimization tools Casadi and Yalmip, with solvers Bonmin and Fmincon, the optimal configuration includes a 13 kWh battery capacity and a 300 m power transmitter distributed across five bus stop areas. The second scenario employs a chance-constrained optimization approach for an isolated solar photovoltaic (PV) and battery energy storage system (BESS). This system is designed to reliably meet the BEBs' energy requirements throughout the day, considering different seasonal data (winter, summer, all seasons/year-round). The optimization results for the PV and BESS capacities vary with the seasons: 394.247 kW and 2012.6 kWh using summer data, 1762.1 kW and 2738.2 kWh using winter data, and 1610.8 kW and 2741.9 kWh using year-round data. Additionally, the paper examines the impact of varying bus fleet sizes on the optimal battery size and power transmitter combination using a real-world example of the bus route between Khalifa City and Abu Dhabi Downtown in the UAE. The findings suggest that larger batteries with fewer or no charging stations are more economical for smaller fleets. Conversely, as the fleet size increases, a combination of smaller battery sizes and a greater number (and length) of QWC (power transmitters) becomes more cost-effective. This research offers significant insights into the efficient deployment of QWC stations and the integration of renewable energy and energy storage for sustainable urban electric bus networks. The proposed optimization models provide a systematic approach to designing and operating charging infrastructure, contributing to sustainable urban transportation systems. Moreover, the study highlights the influence of seasonal data on PV system sizing and costs.

Bidens pilosa extract as a corrosion inhibitor on 1008 carbon steel in neutral medium

The aim of this work is to evaluate the performance of Bidens pilosa extract as a corrosion inhibitor for 1008 carbon steel in a neutral medium of 0.1 M NaCl. The research has been accomplished by weight loss measurements, linear polarization resistance (Rp) monitoring and electrochemical impedance spectroscopy (EIS). Phytochemical analysis and Fourier transform infrared spectroscopy were performed to determine bioactive components and detect the main functional groups of Bidens pilosa extract, respectively. The morpholo­gical characterization of the substrate was carried out by optical microscopy (OM). Different isotherms were evaluated to understand more clearly the adsorption mechanism of the inhibitor extract molecules on the surface of the 1008 carbon steel substrate, and the best fit was obtained for the Langmuir isotherm. The results showed that the corrosion rate decreased with an increase of the concentration of the inhibitor up to 1000 ppm, reaching a maximum efficiency value of 82.9 % from gravimetric tests, and 73.1 % from the fitting of EIS data to an equivalent electric circuit. The calculated thermodynamic parameters suggested the formation of a monolayer of inhibitor molecules on the metal surface. The ΔGoads value (-22.8 kJ mol-1) determined from the Langmuir isotherm model indicated that adsorption of the inhibitor molecules on the substrate surface follows a physisorption mechanism. This research revealed that Bidens pilosa extract can be used as a corrosion inhibitor and emerges as an alternative to replace synthetic corrosion inhibitors that are harmful to health and cause damage to the environment.

A Survey of Multi-Agent Systems for Smartgrids

This paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems.

Strategic Insights into Integrated Photonics: Core Concepts, Practical Deployments, and Future Outlook

Integrated photonics is a cutting-edge field that merges optics and electronics on a single microchip, revolutionizing how we manipulate and transmit light. Imagine traditional bulky optical systems condensed onto a chip smaller than a fingernail, enabling faster communication, more efficient sensors, and advanced computing. At its core, integrated photonics relies on guiding light through waveguides etched onto semiconductor substrates, analogous to how wires conduct electricity in traditional electric circuits. These waveguides can route, modulate, and detect light signals with unprecedented precision and speed. This technology holds immense promise across various domains. Despite its immense potential, integrated photonics faces challenges, including manufacturing complexities and integration with existing electronic systems. However, ongoing research and advancements continue to push the boundaries, promising a future where light-based technologies seamlessly integrate into our everyday lives, powering a new era of innovation and connectivity.

Frequency-dependent electrochemical breakdown of hydrogel ionotronics

When an ionic conductor meets an electronic conductor, ions and electrons couple at the interface between ionic and electronic circuits, enabling a class of devices: ionotronics. Such a coupling gives innovations but can cause electrochemical breakdown. Rapid advances in hydrogel ionotronics, in which hydrogel serves as stretchable, transparent, ionic conductor, highlight an urgent need: a general approach and criterion to study electrochemical breakdown under AC condition. Here we study the breakdown behavior of a metal-hydrogel-metal ionotronic system subject to alternating voltages of various frequencies and amplitudes. First, we apply a sinusoidal voltage to the ionotronic system which is connected in series to a load resistor, and compare the waveforms of the applied voltage and the output voltage between the two ends of the load resistor at various frequencies and amplitudes. Electrochemical breakdown tends to take place at low frequencies and high amplitudes, and causes distortion of waveform of the output voltage. Next, we develop an electric circuit model, in which each hydrogel-metal junction is modeled as a constant phase element in parallel with a leakage resistor and the hydrogel is modeled as a resistor. Our experiments show that the electrochemical window is insensitive to the frequency and amplitude of applied voltage, as well as to the concentration of ions in the hydrogel. Through a combination of experiment and theory, we further propose a frequency-amplitude phase diagram. Different from DC condition, the breakdown behavior under AC condition depends not only on the amplitude of applied voltage, but also on the frequency of applied voltage. We also show that electrochemical reaction can be retarded by stretching the hydrogel and by increasing the frequency. It is hoped that this work will guide the development of stable and reliable hydrogel ionotronic devices.

Electrochemical properties of novel redox active electrode coatings based on heterocyclic polyazines and its nanocomposites with carbon nanomaterials

This work aims to study electrode materials based on unsubstituted polyazines and composites which will allow further advancing both in the molecular design of redox polymers, composites and materials and evaluating their potential applicability. The polymers were prepared by two-phase chemical oxidative polymerization. Polymer-carbon nanocomposites (NC) based on unsubstituted polyphenoxazine were prepared for the first time. The physicochemical and electrochemical properties were compared for NCs based on polyphenothiazine (PPTA), polyphenoxazine (PPOA) and carbon nanomaterials (CNM). The used CNMs comprise reduced graphene oxide (RGO), single-walled and multi-walled carbon nanotubes (SWCNT and MWCNT). The NC structure and morphology were studied by means of FT IR spectroscopy, XRD, FE-SEM and TEM. The limited solubility of some monomers prevents their electrochemical deposition on electrodes. Therefore, to study the electrochemical, spectroelectrochemical behavior and electrochemical impedance of novel redox active electrode coatings, suspensions of synthesized polymers or their NCs were applied onto electrodes and dried. The effect of NC composition on the conductivity and redox activity thereof was discovered. In general, composites have a higher conductivity than individual polymers. Despite differences in electrical conductivity, all samples exhibited electrocatalytic behavior demonstrated for example of Fe(II)/Fe(III) cyanide redox transition on a planar electrode in a K3[Fe(CN)6] solution in 1 M H2SO4. The electronic absorption spectra were recorded in-situ in an electrochemical cell by varying the working electrode potential and showed an electrochromic effect for PPTA and CNM/PPTA. The proposed common equivalent electric circuit allowed explaining the different electrochemical properties of polymers and CNM composites thereof.

Parameters Estimation of Photovoltaic Modules Using Differential Evolution Metaheuristic Method

This paper presents the analysis of the differential evolution (DE) metaheuristic method, which is used to estimate some parameters employed in the equivalent electrical circuit that represents the photovoltaic (PV) cell model. The DE algorithm is an evolutionary algorithm that can be designed to search global optimal points with fast convergence time and present accurate results, requiring few control variables to be set. The accuracy of reproducing the characteristics of the PV module depends on the knowledge of some PV cell model parameters, such as series and shunt resistances, diode ideality factor, and diode reverse saturation current. Once the referred parameters are not usually given in the manufacturers' datasheet, their determination can be achieved by the DE algorithm, employing data and the current-voltage (I-V) characteristic curve provided by the PV module manufacturers. The technique is designed to minimize the error between the reference I-V curve and the I-V curve reproduced by the DE algorithm. Through simulation results, the feasibility of the DE algorithm to estimate the desired parameters of a PV cell is validated, and the algorithm's performance to reach the best solution with fast convergence and a small iteration number is evaluated.

PEM electrolysis: Degradation study of N1110 assemblies for the production of green hydrogen

The production of hydrogen through proton exchange membrane water electrolysis (PEMWE) is seen as a key solution for sustainable energy generation. However, the durability and stability of Proton Exchange Membrane (PEM) cells components, particularly Membrane-Electrode Assemblies (MEAs), remain crucial challenges to overcome for scalability. In this study, the degradation of a 25 cm2 CCM-type MEA was systematically investigated, using N1110 as the polymeric electrolyte and an anodic and cathodic loading of 3 mg/cm2 of PtB. A PEM electrolysis test bench was subjected to controlled operating conditions corresponding to 2 V and 60 °C for two stages of 168 h each, conducting a detailed analysis of performance behavior before, during, and after each degradation period. Electrochemical techniques were employed to characterize the performance stability: chronoamperometry, linear polarization curves, and electrochemical impedance spectroscopy (EIS). The EIS experimental results were fitted to equivalent electrical circuits and the parameters corresponding to ohmic resistances, charge transfer, and diffusional processes were determined. An increase in the resulting time constants and ohmic resistance was depicted because of the degradation.After a straightforward treatment rinsing the assembly with a 1 mol/L H2SO4 solution, the MEA partially regained its initial performance, depicting a degradation rate six times slower than one observed before regeneration in acidic media. This point suggests that the temporary decline in performance of the MEA is primarily due to reversible contamination.

Distribution Expansion Planning of Electrical Networks Considering Electrical Losses and Financial Losses Due to Voltage Sags and Interruptions

Currently, distribution system planning is crucial to meet both the present and future electricity needs of customers with minimal investments. This paper introduces a novel methodology to solve the distribution expansion planning problem by employing a heuristic based on genetic algorithms (GA). The proposed GA aims to minimize investment costs, electrical losses, and financial losses due to voltage sags and power interruptions. This GA is applied in the 18-busbar network with three planning stages. The results show that the GA deals with efficiency with the multi-objective problem obtaining solutions with reduced iterations and with minimum investments considering electrical losses, and financial losses.

A digital twin framework for efficient electric power restoration and resilient recovery in the aftermath of hurricanes considering the interdependencies with road network and essential facilities

The community's resilience in the face of natural hazards relies heavily on the rapid and efficient restoration of electric power networks, which plays a critical role in emergency response, economic recovery, and the functionality of essential lifeline and social infrastructure systems. Leveraging the recent data revolution, the digital twin (DT) concept emerges as a promising tool to enhance the effectiveness of post-disaster recovery efforts. This paper introduces a novel framework for post-hurricane electric power restoration using a hybrid DT approach that combines physics-based and data-driven models by utilizing a dynamic Bayesian network. By capturing the complexities of power system dynamics and incorporating the road network's influence, the framework offers a comprehensive methodology to guide real-time power restoration efforts in post-disaster scenarios. A discrete event simulation is conducted to demonstrate the proposed framework's efficacy. The study showcases how the electric power restoration DT can be monitored and updated in real-time, reflecting changing conditions and facilitating adaptive decision-making. Furthermore, it demonstrates the framework's flexibility to allow decision-makers to prioritize essential, residential, and business facilities and compare different restoration plans and their potential effect on the community.

Resistance distance and sharp bounds of two-mode electrical networks

Electrical networks are ubiquitous in our daily lives, ranging from small integrated circuits to large-scale power systems. These networks can be easily represented as graphs, where edges represent connections and vertices represent electric nodes. The concept of resistance distance originates from electrical networks, with this term used because of its physical interpretation, where every edge in a graph G is assumed to have a unit resistor. The applications of resistance distance extend to various fields such as electrical engineering, physics, and computer science. It is particularly useful in investigating the flow of electrical current in a network and determining the shortest path between two vertices. In this work, we have investigated seven different resistance distance-based indices of bipartite networks and derived general formulae for them; the sharp bounds with respect to these resistance distance indices are also identified. Additionally, we introduced a novel resistance distance topological index, the Multiplicative Eccentric Resistance Harary Index, and derived general formula for it. The sharp bounds with respect to this newly introduced index are also identified for bipartite networks.

A memristive circuit for self-organized network topology formation based on guided axon growth

Circuit implementations of neuronal networks so far have been focusing on synaptic weight changes as network growth principles. Besides these weight changes, however, it is also useful to incorporate additional network growth principles such as guided axon growth and pruning. These allow for dynamical signal delays and a higher degree of self-organization, and can thus lead to novel circuit design principles. In this work we develop an ideal, bio-inspired electrical circuit mimicking growth and pruning controlled by guidance cues. The circuit is based on memristively coupled neuronal oscillators. As coupling element, we use memsensors consisting of a general sensor, two gradient sensors, and two memristors. The oscillators and memsensors are arranged in a grid structure, where oscillators and memsensors realize nodes and edges, respectively. This allows for arbitrary 2D growth scenarios with axon growth controlled by guidance cues. Simulation results show that the circuit successfully mimics a biological example in which two neurons initially grow towards two target neurons, where undesired connections are pruned later on.

Investigation of solution structures in a nonlinear electric transmission network incorporating dissipative elements through physical phenomena analysis

This paper presents an innovative approach to solving the voltage equation in a multi-coupled nonlinear transmission network with dissipative elements. By employing the G′G2 -expansion method and fractional derivatives, we explore the behavior of current and voltage, considering memory effects. Our investigation delves into the impact of fractional calculus on signal transmission, revealing insights into period, amplitude, and propagation direction. Additionally, we analyze the influence of obliqueness angles and various fractional-order derivatives on voltage waveforms Stability analysis demonstrates the significant role of dissipative elements and modulation parameters on signal stability and network resistance to interference. Furthermore, we examine the effects of free parameters on signal transmission characteristics. Our findings highlight the intricate interplay between fractional order, wave obliqueness, and physical parameters on voltage propagation, addressing gaps in previous research.

Compensation of capacitive currents in high-throughput dielectrophoretic separators

Separation and classification are important operations in particle technology, but they are still limited in terms of suspended particles in the micrometer and nanometer size-range. Electrical fields can be beneficial for sorting such particles according to material properties. A mechanism based on strong and inhomogeneous fields is dielectrophoresis (DEP). It can be used to separate microparticles according to their material properties, such as conductivity and permittivity, by selectively trapping one particle type while the other can pass the separator. Conventional DEP-separators show either a limitation in throughput or frequency bandwidth. A low throughput limits the economical feasibility in many cases. A lower frequency bandwidth limits the variety of materials that can be sorted by DEP. To separate semiconducting particles from a mixture containing particles with higher conductivity according to their material, high frequencies are required. Possible applications are the separation of semiconducting and metallic carbon nanotubes or the separation of carbon-coated lithium iron phosphate particles from graphite in the recycling process of spent lithium-ion batteries. In this publication, we aim to display how to tune the electrical impedance of a high-throughput DEP separator based on custom-designed printed circuit boards to increase its frequency bandwidth. By adding inductors to the electrical circuit, we were able to increase the frequency bandwidth from 500 kHz to over 11 MHz. The experiments in this study act as proof-of-principle. Furthermore, a non-deterministic way to increase the impedance of the setup is shown, yielding a maximum frequency of 39.16 MHz.

Magnetic field and initial stress on a rotating photothermal semiconductor medium with ramp type heating and internal heat source

This manuscript addresses a significant research gap in the study by employing a mathematical model of photo thermoelastic wave propagation in a rotator semiconductor medium under the effect of a magnetic field and initial stress, as well as ramp-type heating. The considered model is formulated during the photothermal theory and in two-dimensional (2D) electronic-elastic deformation. The governing equations represent the interaction between the primary physical parameters throughout the process of photothermal transfer. Computational simulations are performed to determine the temperature, carrier density, displacement components, normal stress, and shear stress using the application of Lame’s potential and normal mode analysis. Numerical calculations are carried out and graphically displayed for an isotropic semiconductor like silicon (Si) material. Furthermore, comparisons are made with the previous results obtained by the others, as well as in the presence and absence of magnetic field, rotation, and initial stress. The obtained results illustrate that the rotation, initial stress, magnetic field, and ramp-type heating parameter all have significant effects. This investigation provides valuable insights into the synergistic dynamics among a magnetization constituent, semiconducture structures, and wave propagation, enabling advancements in nuclear reactors' construction, operation, electrical circuits, and solar cells.

Optimal Placement of Multiple Sources in a Mesh-Type DC Microgrid Using Dijkstra’s Algorithm

This research paper introduces an optimization methodology for the strategic electric sources’ placement at multiple positions in a DC islanded microgrid characterized by a mesh network, aiming to minimize line losses while considering minimal cable weight. The DC microgrid studied in this paper is composed of PV panels, batteries, a diesel generator, and 20 residential loads. Employing Dijkstra’s algorithm, a graph algorithm used in Google Maps, the study identifies the shortest path (resistance) between potential source nodes and various variable loads within a predefined electric distribution mesh network topology. This study focuses on active power considerations and offers valuable insights into the placement optimization of multiple sources’ positions in DC microgrid mesh networks. A key contribution of this paper lies in the ranking of source node positions based on minimal to maximal line losses, taking into consideration optimal cable weights, while using MATPOWER to validate sources’ ranking based on Dijkstra’s hypothesis. The research further includes a techno-economic study to assess the viability of sources’ placement at multiple positions within the mesh network, comparing it with the optimal placement scenario involving a single position for all sources. This methodology serves as a valuable resource for system designers and operators aiming to minimize line losses and optimize energy distribution in DC microgrids in a mesh topology.

Electron Leaks in Biophotovoltaics: A Multi‐Disciplinary Perspective

AbstractBiophotovoltaics (BPV), which exploits the natural oxygenic photosystem for energy production, provides a sustainable solution to produce carbon neutral or negative energy from sunlight to meet the growing global energy demand. BPV integrates oxygenic photoautotrophic microorganisms in an electrochemical cell, and harvests the water‐splitting derived photosynthetic electrons to an electrical circuit. Here e. g. electricity or H2, etc, can be produced, thus directly coupling sunlight and water to energy. Despite of the rapid development in the past decade, the system efficiency of BPV still needs magnitude‐level improvement for practical applications. In this perspective paper, we aim to address the electron transfer pipeline in BPV starting from the water splitting by the living whole‐cell catalysts to external electron sinks (i. e. mediator/anode) and eventually to the cathode, from multidisciplinary aspects. We calculated the electron leaks along the electron transfer pipeline to different metabolic electron sinks, and prospectively predicted an untapped potential for extracellular electron transfer rate. BPV could potentially reach an energy efficiency that is two orders of magnitude higher than its current status. An interdisciplinary research approach, that should combine systems and synthetic biology, bioprocess engineering and material science, among others, is proposed to broach the upper boundary of BPV technology.