Terminal Currents Research Articles

Inhibition of Ionic Currents by Fluoxetine in Vestibular Calyces in Different Epithelial Loci.

Previous studies have suggested a role for selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine (Prozac®) in the treatment of dizziness and inner ear vestibular dysfunction. The potential mechanism of action within the vestibular system remains unclear; however, fluoxetine has been reported to block certain types of K+ channel in other systems. Here, we investigated the direct actions of fluoxetine on membrane currents in presynaptic hair cells and postsynaptic calyx afferents of the gerbil peripheral vestibular system using whole cell patch clamp recordings in crista slices. We explored differences in K+ currents in peripheral zone (PZ) and central zone (CZ) calyces of the crista and their response to fluoxetine application. Outward K+ currents in PZ calyces showed greater inactivation at depolarized membrane potentials compared to CZ calyces. The application of 100 μM fluoxetine notably reduced K+ currents in calyx terminals within both zones of the crista, and the remaining currents exhibited distinct traits. In PZ cells, fluoxetine inhibited a non-inactivating K+ current and revealed a rapidly activating and inactivating K+ current, which was sensitive to blocking by 4-aminopyridine. This was in contrast to CZ calyces, where low-voltage-activated and non-inactivating K+ currents persisted following application of 100 μM fluoxetine. Additionally, marked inhibition of transient inward Na+ currents by fluoxetine was observed in calyces from both crista zones. Different concentrations of fluoxetine were tested, and the EC50 values were found to be 40 µM and 32 µM for K+ and Na+ currents, respectively. In contrast, 100 μM fluoxetine had no impact on voltage-dependent K+ currents in mechanosensory type I and type II vestibular hair cells. In summary, micromolar concentrations of fluoxetine are expected to strongly reduce both Na+ and K+ conductance in afferent neurons of the peripheral vestibular system in vivo. This would lead to inhibition of action potential firing in vestibular sensory neurons and has therapeutic implications for disorders of balance.

0.35 V Subthreshold Bulk-Driven CMOS Second-Generation Current Conveyor

This study describes a high-performance second-generation Current Conveyor (CCII) operating at 0.35 V and achieving rail-to-rail operation at the Y terminal and class AB current drive at the X and Z terminals. The solution utilizes a low-voltage subthreshold bulk-driven CMOS OTA that was experimentally developed earlier, making systematic use of body terminals to improve small-signal and large-signal performance. The circuit has a high open-loop voltage gain and uses cascoded current mirror topologies, resulting in precise voltage and current transfer with bandwidths of 1.33 MHz and 2.13 MHz, respectively. The CCII offers a linear current drive up to 2.5 µA while consuming a total quiescent current of 2.86 µA (758 nA in the output branches), displaying one the highest figures of merit in terms of current utilization for sub 1 V solutions.

Risk analysis of lithium battery energy storage systems under typical failures

Abstract With the rapid increase in the proportion of new energy installed capacity, to solve the problem of new energy output volatility, lithium-ion battery energy storage has developed rapidly by its electrical characteristics and economic advantages and has become a hot spot for the large-scale application of electrochemical energy storage, but it is also accompanied by safety problems such as battery short-circuit and thermal runaway. Based on the typical structure of the lithium battery energy storage system, this paper establishes a complete simulation model of the lithium battery energy storage system, calculates the change rule of battery system electrical parameters inside the battery module under different types of short-circuit faults, and summarizes the fault characteristics and risks of lithium battery energy storage system under different faults. The study shows that short-circuit faults inside the battery module will cause significant fluctuations in the terminal voltage and current of the battery system. The internal short-circuit faults will increase the inter-cluster circulating current. In contrast, the larger short-circuit current will not only cause the local battery cells to overcharge and over-discharge but also trigger the thermal runaway of the battery system.

A Datasheet-Driven Electrothermal Averaged Model of a Diode–MOSFET Switch for Fast Simulations of DC–DC Converters

The design of modern power electronics converters requires accurate electrothermal device models enabling a straightforward parameter estimation and fast, yet accurate, circuit simulations. In this paper, a novel electrothermal averaged model of a diode–MOSFET switch for fast analysis of DC–DC converters is proposed. The model has the form of a SPICE-compatible subcircuit and allows computing in a very short simulation time the DC characteristics of the converter, the waveforms of the terminal voltages and currents of the semiconductor devices, as well as their junction temperatures, both in CCM and DCM.. All the input data required by the parameter estimation procedure can be taken from the datasheets of components. The correctness of the proposed approach is experimentally verified for a buck converter chosen as a case-study. A generally good agreement between measurements and simulations is obtained; as an example, the absolute error in assessing the MOSFET junction temperature does not exceed 12 °C within the whole range of switching frequency of the converter, while the commonly used PLECS model considerably underestimates it.

A Kalman Filter-Based Distributed Cyber-Attack Mitigation Strategy for Distributed Generator Units in Meshed DC Microgrids

To ensure fast dynamics and the stability of multiple distributed generator units (DGUs) in DC microgrids, communication links among the controllers of DGUs are generally adopted. However, those communication channels are vulnerable to cyber-attacks. To alleviate this hassle, a Kalman Filter (KF)-based distributed cyber-attack mitigation strategy, which is highly involved in both primary and secondary control, is proposed in this paper. The KF, as a robust state estimator, is utilized to accurately estimate the authentic terminal voltages and currents of the DGUs. Based on the discrepancies between the estimated and measured parameters of the systems under cyber-attacks, the proposed control can adaptively compensate the attack signals via an adaptive proportional integral (API) controller and a fractional API (FAPI) controller in cyber-attack-mitigation layers. The main advantage of using the proposed control scheme compared to conventional schemes is the fast dynamic response. The simulation results verify this merit by comparing the adopted KF and comparing it with conventional artificial neural networks (ANN), while the experimental results validate that effectiveness of the proposed control and showcase the superiority of the FAPI control in terms of its perfect compensation for different types of cyber-attacks.

The thickness and width of the intrinsic region effected electrical properties of GaAs p-i-n photodiodes

The thickness of the p-i-n photodiode's p and n contacts, as well as the width of the semiconductor device, affect the conductivity performance of the diode. In this study, the impact of thickness and width variations of GaAs p-i-n photodiode devices on their performance was investigated using the COMSOL Multiphysics software, which enables simulations of multiple physical fields. The terminal current and field strength at various positions were calculated for different thickness and width values. The mechanisms behind the influence of thickness and width variations on the conductivity performance were explained, and a detailed description of the distribution of terminal current and electric field magnitude was provided. Based on experimental data, the optimal thickness and width for achieving peak conductivity performance of the diode under material-saving conditions were determined, providing theoretical guidance for the preparation of efficient and stable GaAs p-i-n photodiodes.

Toward a simple topological model of a three-phase transformer including deep saturation conditions

PurposeThis paper aims to propose a method of parametrizing topological transformer model at high flux densities in the core.Design/methodology/approachThe approach proposed is based on terminal voltages and currents measured in a special purpose saturation test whose data are combined with typical saturation curves of grain-oriented electrical steels; the modeling is carried out in the ATPDraw program.FindingsThe authors corroborate experimentally the necessity of dividing the zero sequence impedance between all transformer phases and propose a method of the individual representation of the legs and yokes. This eliminates the use of nonexistent leakage inductances of primary and secondary windings.Practical implicationsThe presented modeling approach can be used for predicting inrush current events and in the evaluation of the impact caused by geomagnetically induced currents (GICs).Originality/valueThe proposed approach is completely original and will contribute to a better understanding of the transients occurring in a transformer under abnormal conditions, such as inrush current events and GICs.

Experimental investigation to evaluate the performance profile of a small-scale standalone concentrated solar parabolic dish Stirling system

In this study, a small-scale solar parabolic dish Stirling system (SPDSS) is designed, developed and experimentally operated to generate electricity. The developed system is composed of a solar concentrator, a Stirling engine-generator set, and dual-axis tracking mechanism. The locally assembled Stirling engine is air-filled, double-acting gamma, with a nominal power rating of 40 W. There are six essential output parameters including engine displacer temperature, average engine speed, generator terminal voltage, load current, electrical power and overall efficiency of the system, which have been measured experimentally to assess global performance of the system. The experimental results revealed that the developed system could yield a maximum engine displacer temperature of 275°C, average engine speed of 261 RPM, peak generator no-load terminal voltage of 31.3 V, maximum load current of 2.67 amps, maximum electrical power of 17 W and overall efficiency of 3.4%. The experimental results, for the purpose of validation, have also been compared with simulation results and found in good agreement.

Phase-Shift Control With Unified PWM/PFM for Improved Transient Response in a Bidirectional Dual-Active-Bridge DC/DC Converter

Determining how to efficiently obtain superior transient response under both unidirectional power-changing and power-reversing operations in a bidirectional dual-active-bridge (DAB) dc/dc converter is a challenging problem. The traditional direct phase-shifting scheme would induce a transient dc current bias in the high-frequency link of the DAB, and therefore lead to some adverse issues, such as magnetic saturation and increased current stress. In addition, the ac transient can propagate to the dc terminates and give rise to a worse transient response in a closed-loop control system. In this study, a fast phase-shift control is proposed which can execute all the possible power changes in a unified manner. By properly operating the primary bridge with pulsewidth modulation (PWM) and the secondary bridge with pulse frequency modulation (PFM), the DAB converter can complete the phase-shifting operation fast without a dc current bias in the ac link. A closed-loop control based on the hybrid PWM/PFM is further designed to regulate the primary dc current with improved transient performance. Since both PWM and PFM are standard, the whole control algorithm can be implemented efficiently in practical applications. Experimental results are provided to show that the power of the DAB converter can be governed bidirectionally with the proposed phase-shift control. The dc terminal current can be changed to both polarities with fast transient and no apparent overshoot or oscillation.

Converter valve-level fault location method based on the temporal differences among horizontal and longitudinal valve states

AC fault or converter fault could lead to LCC-HVDC converter protection operation, and converter protection outlet involves a random bypass pair scheme, which would expand the fault. To address this issue, a converter valve-level fault location method based on temporal differences among horizontal and longitudinal valve states is proposed. The research focuses on the inverter as the object, and analyzes the circuit state characteristics at the different fault locations within converter. In the horizontal direction of converter structure, conduction widths of AC current are constructed based on the location of commutation groups. In the converter longitudinal direction, temporal features of conduction widths are constructed based on single bridge or two bridges. Firstly, fault area is identified based on the amplitude characteristics of converter terminal currents and the abnormal characteristics of valve states. Secondly, converter internal fault is located by using the difference in the horizontal conduction width and the longitudinal temporal features between two bridges. Thirdly, the faulty phase or faulty valve is located as the valve that has the greatest virtual conduction width within the time window interval. Finally, simulations and field record data verify the validity of the proposed fault location method, and show that the proposed method has strong adaptability.

Improved power sharing in inverter based microgrid using multi-objective optimization

The voltage-source inverter has an important role in electrical power sharing in microgrids, although, it requires a tight control and optimization technique to enhance performance and to improve power sharing. A sudden load change is a common phenomenon which may lead to fluctuations in the terminal voltages and load currents and eventually the system might become unstable. The PI control law represents one of the preferred control algorithms in industry, meanwhile its parameters need to be tuned to ensure its superiority. Two multi-objective optimization algorithms, the Genetic Algorithm, and the Water Cycle Algorithm, are proposed in this article to tune the controller gains. A microgrid consisting of two-parallel inverters is used to investigate the performance of the proposed tuning techniques where the system is analyzed in the dq-frame and the Space Vector Pulse Width Modulation was undertaken as a switching technique. MATLAB/SIMULINK simulations demonstrated the improved performance of the proposed approach.

An imperative role of studying existing battery datasets and algorithms for battery management system

Numerous portable technologies, including electric vehicles, cell phones, and laptops, are powered by batteries. The use of batteries is increasing due to the widespread usage of battery energy storage in the generation of renewable energy. This has also resulted in an increase in the number of negative incidents related to batteries and had a significant negative economic impact on industries. The shortcomings in the traditional monitoring of lithium-ion batteries have been overcome through the implementation of advanced technologies. However, few studies have discussed the use of distinct datasets in the implementation of an intelligent battery management system (BMS). This paper presents a discussion on the choice of dataset variables and applied algorithms for the implementation of effective BMS with artificial intelligence (AI) and machine learning (ML). The study analyzed the use of different datasets, including the National Aeronautics and Space Administration (NASA) battery dataset, to improve BMS. It found that the dataset variables must include the terminal voltage, terminal current, charge current, charge voltage, internal resistance, temperature, and cycle to calculate the state of health (SoH). In future, BMS hardware will be developed to obtain more precise results using AI and ML-based prediction models, utilizing the selected dataset characteristics and variables to achieve longer battery life.

State of charge estimation and error analysis of lithium-ion batteries for electric vehicles using Kalman filter and deep neural network

The lithium-ion battery has a great significance in meeting the growing demand for Electric Vehicles (EVs) due to its higher energy density, longer life cycle, and notable nominal voltage and capacity. One crucial parameter for lithium-ion batteries is the State of Charge (SOC), which represents the available capacity and ensures that the system operates in a secure and reliable mode for EVs. SOC plays a significant role in the Battery Management System (BMS). This research aims to propose an Equivalent Circuit Model (ECM) based on Kalman filtering method and a data-driven technique, Deep Feed-Forward Neural Network (DFNN), for accurate SOC estimation of Electric Vehicle Battery (EVB). Initially, lithium-ion battery parameters are identified using a second-order RC (2-RC) Equivalent Circuit Model (ECM). Subsequently, battery modeling is performed, and various operating conditions such as terminal voltage, load current, and temperature are measured to obtain initial values for the filtering method used in SOC estimation. These operating conditions are crucial for ensuring safe and efficient charging and discharging of lithium-ion batteries. Based on the identified ECM parameters, SOC estimation error and bound error are then minimized using Kalman Filter (KF), Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) techniques. These filtering methods are employed to accurately estimate the SOC of the battery. The results demonstrate that the proposed model based on KF and EKF algorithms estimates SOC bound error within 2.5 % - –2 % and estimation error <1.5 % - –0.7 %. On the other hand, the UKF estimates a SOC bound error of 1.5 % and an estimation error of 0.5 %, proving the algorithm's efficiency and reliability. Particularly, this estimation error rejects measurement noise and parametric uncertainties for lithium-ion batteries to drive EVs with efficacy using UKF. Hence, the UKF algorithm estimated SOC has low estimation error, ensuring more accurate results. Finally, data-driven, DFNN method is implemented for accuracy enhancement of SOC estimation with trained 20 iterations and epochs data. Using this method, the SOC estimation accuracy is satisfactory with only 0.04 % Root Mean Squared Error (RMSE). The validation results indicate that the model-based filtering method is an effective method for SOC estimation to be applicable. In contrast, in a novel data-driven approach, SOC estimation accuracy is improved by approximately 0.46 %.

Polarity of current area based active injection protection scheme for hybrid MMC-HVDC grids

With the massive increase of power electronic devices to facilitate renewable integration, the system’s fault characteristics will be dramatically changed due to the different control strategies adopted by converters. As a result, the dependability and security of the traditional protection can be severely compromised. Considering the high controllability of power electronic converters, this paper presents an active injection protection scheme to effectively identify the fault. By analyzing the traveling wave (TW) refraction and reflection characteristics under different fault conditions, the variation of the measured terminal current caused by the TW is revealed. Then, a rectangular wave is injected by the hybrid modular multilevel converters (MMC) for a short time after the fault ride-through process. By introducing the concept of the polarity of the current area, the proposed protection scheme only needs to measure the terminal current without any complex signal extracting and processing algorithm to identify the internal and external faults. The feasibility and effectiveness of the proposed protection scheme are verified by realistic case studies with simulation conducted in the PSCAD platform.

Investigation of the Gate Degradation Induced by Forward Gate Voltage Stress in p-GaN Gate High Electron Mobility Transistors.

In this work, we investigated the degradation of the p-GaN gate stack induced by the forward gate voltage stress in normally off AlGaN/GaN high electron mobility transistors (HEMTs) with Schottky-type p-GaN gate. The gate stack degradations of p-GaN gate HEMTs were investigated by performing the gate step voltage stress and the gate constant voltage stress measurements. In the gate step voltage stress test, the positive and negative shifts of threshold voltage (VTH) depended on the range of the gate stress voltage (VG.stress) at room temperature. However, the positive shift of VTH in the small gate stress voltage was not observed at 75 and 100 °C and the negative shift of VTH was started from a lower gate voltage at a high temperature compared to room temperature. In the gate constant voltage stress test, the gate leakage current increased with three steps in the off-state current characteristics as the degradation progressed. To investigate the detailed breakdown mechanism, we measured the two terminal currents (IGD and IGS) before and after the stress test. The difference between the gate-source current and the gate-drain current in the reverse gate bias indicated that the increase of the leakage current was attributed to the degradation between the gate and the source while the drain side was not affected.

Synaptotagmin-7 facilitates acetylcholine release in splanchnic nerve-chromaffin cell synapses during nerve activity

Disturbances that threaten homeostasis elicit activation of the sympathetic nervous system (SNS) and the adrenal medulla. The effectors discharge as a unit to drive global and immediate changes in whole-body physiology. Descending sympathetic information is conveyed to the adrenal medulla via preganglionic splanchnic fibers. These fibers pass into the gland and synapse onto chromaffin cells, which synthesize, store, and secrete catecholamines and vasoactive peptides. While the importance of the sympatho-adrenal branch of the autonomic nervous system has been appreciated for many decades, the mechanisms underlying transmission between presynaptic splanchnic neurons and postsynaptic chromaffin cells have remained obscure. In contrast to chromaffin cells, which have enjoyed sustained attention as a model system for exocytosis, even the Ca2+ sensors that are expressed within splanchnic terminals have not yet been identified. This study shows that a ubiquitous Ca2+-binding protein, synaptotagmin-7 (Syt7), is expressed within the fibers that innervate the adrenal medulla, and that its absence can alter synaptic transmission in the preganglionic terminals of chromaffin cells. The prevailing impact in synapses that lack Syt7 is a decrease in synaptic strength and neuronal short-term plasticity. Evoked excitatory postsynaptic currents (EPSCs) in Syt7 KO preganglionic terminals are smaller in amplitude than in wild-type synapses stimulated in an identical manner. Splanchnic inputs also display robust short-term presynaptic facilitation, which is compromised in the absence of Syt7. These data reveal, for the first time, a role for any synaptotagmin at the splanchnic-chromaffin cell synapse. They also suggest that Syt7 has actions at synaptic terminals that are conserved across central and peripheral branches of the nervous system.

Sub-synchronous oscillation suppression in MMC-HVDC grid-connected systems under wind farm fluctuation scenarios

In order to solve the problem of sub-synchronous oscillation (SSO) of wind power grid-connected system with modular multilevel converter based on high voltage direct current transmission (MMC-HVDC), which is characterized by frequency drift due to the fluctuation of wind power output and the dynamic interaction of MMC. By considering the coupling characteristics of wind power volatility and the internal harmonic dynamics of MMC, we establish a small-signal model of the interconnection system between the equivalent wind farm and MMC-HVDC, study the dominant factors triggering the system oscillation, and use model predictive control (MPC). The results show that the wind farm output, wind farm terminal voltage and terminal current are the dominant factors leading to SSO frequency drift. It is also verified that the MPC-SSDC control not only effectively suppresses SSOs with single frequency drift, but is also effective when multiple SSOs with different frequencies exist simultaneously. It is shown that MPC-SSDC can self-adapt to the multi-oscillation scenario of wind power grid-connected system to provide frequency and damping support for the system.

Hybrid Space Vector Modulation Scheme for the Multiport Hybrid Converter

This work proposes a hybrid space vector modulation (HSVM) scheme for multiport hybrid converters (MHCs). Moreover, the impact of shifting the auxiliary currents from the main ones is proposed and investigated in order to enhance the converter efficiency. The proposed operational schemes have been implemented in a three-phase 5 kW MHC prototype. It is shown that the proposed HSVM scheme can improve the MHC efficiency by 0.3% at full load with respect to space vector modulation. At partial loads, the improvement is even more significant, reaching +0.7% at 30% of the rated power. A further 0.15% increase in efficiency at full power can be achieved by a 180 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> phase shifting of the auxiliary currents with respect to the main terminal currents, reaching a peak efficiency of 98.5%.

Overvoltage Suppression for Modular Multilevel Converter With Switch Open-Circuit Fault

To enhance reliability and exploit advantages, modular multilevel converters (MMCs) are mostly desired with uninterrupted operational capability during submodule (SM) open-circuit fault. Once an SM fault is located, the closing signal will be delivered to the corresponding bypass switch. However, due to the response delay of mechanical switches, capacitor voltage of the SM with lower switch fault will continue increasing, thus resulting in severe overvoltage and threatening the safety of MMC. In view of this, this article proposes an arm current reshaping-based method to suppress capacitor overvoltage under SM failure. During the period of overvoltage suppression, all the arm currents are reshaped to decrease capacitor charging of the faulty SM and maintain the terminal currents of MMC. Besides, since the capacitor voltage variations are also reshaped by the reshaped arm current, common-mode voltage is injected to satisfy the requirements of arm voltages. Consequently, the degree of SM overvoltage can be alleviated by over 50% without extra hardware investment. Simulation and experimental results confirm the feasibility and validity of the proposed method.

Comparison of decision tree based ensemble methods for prediction of photovoltaic maximum current

• Irradiance can be used as a direct reference generator to avoid tracking oscillations. • Extracting the parasitic resistances in Single Diode Model can assess aging. • Solar panel maximum current is directly proportional to irradiance variations. • AdaBoost is sensitive to noisy datasets and extreme outliers. The intermittent nature of the output power of photovoltaic (PV) systems, in addition to the fast-varying solar irradiance, has prompted the development of fast, accurate, and reliable forecasting techniques. This paper presents a comparative study of five ensemble machine learning methods based on bootstrap aggregating and gradient boosting for PV applications, namely AdaBoost, LightGBM, XGBoost, Random Forest, and CatBoost. A dataset of fast-varying environmental conditions was collected, and the terminal current of the experimental setup was augmented by applying a mathematical model, along with an evolutionary algorithm to extract the parasitic resistances found in the Single Diode Model (SDM) to accommodate for the aging effect. The mathematical model was evaluated for several irradiance and temperature levels against manufacturer Standard Test Conditions (STC), and then variations of environmental conditions were adjusted based on the manufacturer datasheet. CatBoost showed the lowest overall absolute error distribution (with respect to the mean and standard deviation) of all methods, and the best performance in terms of the absolute error (0.25%) and its standard deviation (0.85%) relative to the mathematical model. The AdaBoost method had the highest absolute error (34.5%) and a standard deviation of (15.8%). After hyperparameters tuning, CatBoost still outperformed other methods and showed consistency of high accuracy above 99% in performance with respect to a testing dataset, in addition, to having the largest area under the curve using the trapezoidal rule. Therefore, the CatBoost prediction method is expected to be an effective technique for maximum power point tracking schemes under fast-varying environmental conditions.