Voltage Spikes Research Articles

Single Power-Conversion Active-Clamped AC/DC Converter Employing Si/SiC Hybrid Switch

This article proposes a single power-conversion voltage-fed active-clamped ac/dc converter that uses an Si IGBT/SiC MOSFET hybrid switch. By incorporating two switches at the primary side of the circuit, we removed the full-bridge diode rectifier, and thereby reduced the required number of power devices. Inherent bidirectional active-clamp circuit naturally limits the voltage spike at bottom switches and reuses the energy accumulated in the leakage inductor during both positive and negative grid half-cycles. Also, it offers dual-resonant power transfer paths, so it can deliver the high power with less circuit components. The alternating switching modulation makes one of the bottom switches turn off with hard switching per grid half-cycle while holding other bottom switch on to conduct the current. In contrast, top switches are softly switched with low root-mean-square current. Therefore, the converter with proposed switching modulation enables direct substitution of Si IGBT for an SiC MOSFET at the top on the primary side, and thereby which cuts down the circuit implementation cost. We built a 1 kW prototype converter and confirmed its validity.

STDP-Driven Development of Attention-Based People Detection in Spiking Neural Networks

This correspondence paper provides, to the best of our knowledge, a first analysis of how biologically-plausible spiking neural networks (SNNs) equipped with Spike-Timing-Dependent Plasticity (STDP) can learn to detect people on the fly from non-independent and identically distributed (non-i.i.d) streams of retina-inspired, event camera data. Our system works as follows. First, a short sequence of event data capturing a walking human from a flying drone is forwarded in its natural order to an SNN-STDP system, which also receives teacher spiking signals from the neural activity readout block. Then, when the end of the learning sequence is reached, the learned system is assessed on testing sequences. In addition, we also present a new interpretation of anti-Hebbian plasticity as an over-fitting control mechanism, and provide experimental demonstrations of our findings. This work contributes to the study of attention-based development and perception in bio-inspired systems.

Performance improvement of fuel cell and photovoltaic system

This article considers and studies a hybrid energy storage system as a potential replacement for a utility grid. It also examines its organisational structure. The hybrid energy storage technology is used to ensure a constant supply of convenient grid electricity that is sufficient to handle changing power spikes. Batteries are used to stabilise the surges with measurable variation, whereas a massive capacitor is utilised to stabilise the surges with fast variation. In isolated areas where connecting to the main utility grid is impractical, standalone renewable generation may provide the advantage of a reduced operational cost as well as a reduction in protection fees. In order to encourage non-conventional power production in the overall renewable energy system, advancements in solid oxide fuel-cell technology and solar photovoltaic (PV) technology have also been made. Grid-coupled solar PV energy producing systems are being extensively used worldwide, and solar PV modules are increasingly being used in residential applications linked to the electrical grid.

Utilization of Single Phase Pure Sine Wave Inverters in Electrical Energy Conversion Systems for Solar Panels with Variable Loads

The inverter built in this research is used to convert direct current into alternating current with a size of 3000 watts, which has a DC input voltage of 24 volts. This inverter has a large capacity and can generate enough electricity to run different huge electrical devices or several units at once. It's made utilizing energy-efficient technology to cut down on energy waste during conversion. Furthermore, safeguards such voltage surge and overcurrent prevention guarantee the operational safety of the inverter. The inverter was built using the PWM method and variable loads (resistive load, inductive load and capacitive load), with the aim of evaluating the inverter's ability to handle resistive, inductive and capacitive loads and to see how the inverter responds to load changes dynamically. Based on observations and calculations, higher loads are positively correlated with higher inverter power and efficiency. The inverter has a max output of 664.62 watts and 229 Volt is the output voltage attained. This demonstrates that the EGS002 PSW inverter produces AC voltage of high quality with precise and steady characteristics.

Brain-inspired chaotic spiking backpropagation.

Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation (CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets (e.g. DVS-CIFAR10 and DVS-Gesture) and large-scale static data sets (e.g. CIFAR100 and ImageNet) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics, consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.

High step up DC–DC converter with low conduction losses and reduced switching losses

AbstractThis article presents a novel high step up converter based on the active switch inductor (ASL) technique for renewable energy source applications. The coupled inductor technique provides an opportunity to increase the gain. The proposed converter exploited the leakage inductors to provide zero current switching (ZCS) turn‐on for both switches. Therefore, there is no need for an auxiliary circuit, which simplifies the structure. Also, all diodes turn off under ZCS, which eliminates the reverse recovery problem and results in reduced switching losses. The energy of the leakage inductor is recycled, and the voltage spike on the switches is limited. In addition, all semiconductor voltage stresses are clamped below the output voltage. Hence, components with lower on‐resistance can be utilized to reduce conduction loss. Because of the reduction in power loss, the efficiency rises to 97.2% at 250 W output power. It also benefits from the current sharing of the interleaved structure. The operating principles, design considerations, and comparison to other topologies are discussed. To verify the effectiveness of the proposed converter, a prototype converter with 250 W of output power, 50 V of input voltage, 500 V of output voltage, and 100 kHz of switching frequency is implemented.

Facile synthesis of Ni anchored on N-doped carbon nanotube as cathodic electronic conductive agent for thermal battery

Carbon series material is currently the most prevalent electronic conductive agent of thermal battery cathode. For practical applications, such materials suffer from issues such as light density and low conductivity, which can cause dust pollution and affect formability of electrode. Herein, nickel dispersed by bamboo-like nitrogen-doped carbon nanotube (Ni/NCNT) is conveniently synthesized by pyrolysis of nickel-dicyandiamide complex. Benefiting unique structure and the existence of metallic nickel, the single cell with Ni/NCNT cathodic electronic conductive agent yields a long discharge lifetime, accompanying with outstanding current carrying capacity. Concurrently, the Ni/NCNT presents a certain attenuating voltage spike capacity, and thus improving discharge stability. This study offers a potential alternative to conventional conductive agent and antipeak agent for thermal battery.

Deep learning method for predicting electromagnetic emission spectrum of aerospace equipment

AbstractThis paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short‐term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment.

Memristive Response and Capacitive Spiking in Aqueous Ion Transport through Two-Dimensional Nanopore Arrays.

In living organisms, information is processed in interconnected symphonies of ionic currents spiking through protein ion channels. As a result of dynamic switching of their conductive states, ion channels exhibit a variety of current-voltage nonlinearities and memory effects. Fueled by the promise of computing architectures entirely different from von Neumann, recent attempts to identify and harness similar phenomena in artificial nanofluidic environments focused on demonstrating analogue circuit elements with memory. Here we explore aqueous ionic transport through two-dimensional (2D) membranes featuring arrays of ion-trapping crown-ether-like pores. We demonstrate that for aqueous salts featuring ions with different ion-pore binding affinities, memristive effects emerge through coupling between the time-delayed state of the system and its transport properties. We also demonstrate a nanopore array that behaves as a capacitor with a strain-tunable built-in barrier, yielding behaviors ranging from current spiking to an ohmic response. By focusing on the illustrative underlying mechanisms, we demonstrate that realistically observable memory effects may be achieved in nanofluidic systems featuring crown-porous 2D membranes.

Response of sensor-enabled piezoelectric geobelt reinforced soil pullout friction signals

Geosynthetics are widely used in soil reinforcement engineering, the failure modes of which are typically pullout modes. However, current research on monitoring the pullout damage experienced by reinforced soil is limited. Accordingly, in this study, the stress variation laws and signal output characteristics of a sensor-enabled piezoelectric geobelt (SPGB) that can capture the tensile vibration signals of reinforced soil under various tensile failure conditions were examined. It was found that the SPGB captured the response signals under different soil environments, confining pressures, and shear rates. During the pullout friction process, a spike was generated, and the displacement corresponding to the position of the spike increased with an increase of the pullout friction rate. In the clay environment, the spike voltage was related to the confining pressure, while in the sand environment, there was no obvious relationship between the spike voltage and the confining pressure. In the gravel environment, the output voltage of the SPGB fluctuated greatly, due to the embedment of gravel particles, and many negative voltages of large amplitude appeared. The results of this study verify the promising application potential of SPGBs for the integration of soil reinforcement and monitoring, which is significant for further applications of SPGBs in engineering.

Turn-off switching voltage surge analysis with dependence on IGBT cell design

Surge voltage at insulated gate bipolar transistor turn-off switching was analyzed with dependence on cell design parameters. Although drift layer thinning is effective in improving trade-off characteristics between turn-off loss E off and on-state voltage V on, voltage surge is induced due to the quickly expanding depletion layer. Therefore, the surge voltage V surge has also a trade-off relationship with V on at the same E off condition. The origin of the voltage surge was analyzed using TCAD simulation, and the total amount of remaining holes in the drift layer during turn-off switching is a key factor for the V surge. A narrow mesa structure and thick buffer layer are effective for the improvement of trade-off characteristics between V surge and V on. However, the optimum buffer layer thickness depends on the voltage-class due to the speed for punch through.

Vesicular neurotransmitters exocytosis monitored by amperometry: theoretical quantitative links between experimental current spikes shapes and intravesicular structures.

Single cell amperometry has proven to be a powerful and well-established method for characterizing single vesicular exocytotic events elicited at the level of excitable cells under various experimental conditions. Nevertheless, most of the reported characteristics are descriptive, being mostly concerned with the morphological characteristics of the recorded current spikes (maximum current intensities, released charge, rise and fall times, etc.) which are certainly important but do not provide sufficient kinetic information on exocytotic mechanisms due to lack of quantitative models. Here, continuing our previous efforts to provide rigorous models rationalizing the kinetic structures of frequently encountered spike types (spikes with unique exponential decay tails and kiss-and-run events), we describe a new theoretical approach enabling a quantitative kinetic modeling of all types of exocytotic events giving rise to current spikes exhibiting exponential decay tails. This model follows directly from the fact that the condensation of long intravesicular polyelectrolytic strands by high concentrations of monocationic neurotransmitter molecules leads to a matrix structure involving two compartments in constant kinetic exchanges during release. This kinetic model has been validated theoretically (direct and inverse problems) and its experimental interest established by the analysis of the amperometric spikes relative to chromaffin and PC12 cells previously published by some of us.

Analysis and Design of Damping Resistor for Mitigating Surge Voltage and Oscillation in Flyback Converter

Analysis and Design of Damping Resistor for Mitigating Surge Voltage and Oscillation in Flyback Converter

Analysis and Implementation of the crowbar clamping circuit for surge voltage reduction in the DC solid-state circuit breaker

Analysis and Implementation of the crowbar clamping circuit for surge voltage reduction in the DC solid-state circuit breaker

Development of a Low-Energy Mobile Current Waveform Impulse Generator for Application in Low Voltage Surge Arresters

Development of a Low-Energy Mobile Current Waveform Impulse Generator for Application in Low Voltage Surge Arresters

A spiking binary neuron — detector of causal links

Purpose. Causal relationship recognition is a fundamental operation in neural networks aimed at learning behavior, action planning, and inferring external world dynamics. This operation is particularly crucial for reinforcement learning (RL). In the context of spiking neural networks (SNNs), events are represented as spikes emitted by network neurons or input nodes. Detecting causal relationships within these events is essential for effective RL implementation. Methods. This research paper presents a novel approach to realize causal relationship recognition using a simple spiking binary neuron. The proposed method leverages specially designed synaptic plasticity rules, which are both straightforward and efficient. Notably, our approach accounts for the temporal aspects of detected causal links and accommodates the representation of spiking signals as single spikes or tight spike sequences (bursts), as observed in biological brains. Furthermore, this study places a strong emphasis on the hardware-friendliness of the proposed models, ensuring their efficient implementation on modern and future neuroprocessors. Results. Being compared with precise machine learning techniques, such as decision tree algorithms and convolutional neural networks, our neuron demonstrates satisfactory accuracy despite its simplicity. Conclusion. We introduce a multi-neuron structure capable of operating in more complex environments with enhanced accuracy, making it a promising candidate for the advancement of RL applications in SNNs.

High-Throughput Microelectrode Arrays for Precise Functional Localization of the Globus Pallidus Internus.

The globus pallidus internus (GPi) was considered a common target for stimulation in Parkinson's disease (PD). Located deep in the brain and of small size, pinpointing it during surgery is challenging. Multi-channel microelectrode arrays (MEAs) can provide micrometer-level precision functional localization, which can maximize the surgical outcome. In this paper, a 64-channel MEA modified by platinum nanoparticles with a detection site impedance of 61.1 kΩ was designed and prepared, and multiple channels could be synchronized to cover the target brain region and its neighboring regions so that the GPi could be identified quickly and accurately. The results of the implant trajectory indicate that, compared to the control side, there is a reduction in local field potential (LFP) power in multiple subregions of the upper central thalamus on the PD-induced side, while the remaining brain regions exhibit an increasing trend. When the MEA tip was positioned at 8,700 μm deep in the brain, the various characterizations of the spike signals, combined with the electrophysiological characteristics of the β-segmental oscillations in PD, enabled MEAs to localize the GPi at the single-cell level. More precise localization could be achieved by utilizing the distinct characteristics of the internal capsule (ic), the thalamic reticular nucleus (Rt), and the peduncular part of the lateral hypothalamus (PLH) brain regions, as well as the relative positions of these brain structures. The MEAs designed in this study provide a new detection method and tool for functional localization of PD targets and PD pathogenesis at the cellular level.

Issues of Power Quality

Abstract: In this modern era, the electricity dependence for domestic, commercial and industrial purposes has been increasing day to day. The non-linear loads associated will leads to some of the power quality issues. Such Power Quality Issues viz., voltage sag, Interruptions, spikes in voltage, fluctuations etc. briefly discussed in this paper.

A Hardware-Simplified Soft-Start Scheme for Current-Fed Full-Bridge DC-DC Converter

At present, the current-fed full-bridge DC-DC converter still faces two inherent defects, namely, the soft-start issue during the start-up process and the voltage spike issue in normal working conditions. In existing research, a large number of attempts have been reported to overcome these two inherent issues. However, the existing solutions are all based on both redundant soft-start circuits and snubber circuits, and two different circuits are required. As a result, the cost of the current-fed full-bridge DC-DC converter is high, especially because the soft-start circuits are only used during the start-up process. So, to reduce device redundancy, a hardware-simplified soft-start scheme is proposed in this paper. In the proposed scheme, the snubber circuit is reused for the soft-start process based on topology reconfiguration, avoiding additional soft-start circuits to reduce the cost of this DC-DC converter. In this proposed method, the current-fed full-bridge DC-DC converter is operated as the voltage-fed full-bridge DC-DC converter by always turning on the snubber switch during the start-up process. Then, the output capacitor can be charged by slowly increasing the output voltage of the H bridge. Then, when the output voltage is high enough, the current-fed full-bridge DC-DC converter is again operated in the current-fed mode to boost the voltage. Subsequently, the converter characters and control strategy were examined and analyzed. The experimental results verify the feasibility of the proposed hardware-simplified soft-start scheme for the current-fed full-bridge DC-DC converter.

Hybrid Technique for Vibration Control using Synchronized Switch Damping on Inductor Modal Approach and Shape Memory Alloy Integration

Vibration control is a critical aspect of various engineering applications, including automotive and aerospace systems. This research paper presents a novel approach to enhance the performance of the Synchronized Switch Damping on Inductor (SSDI) technique by incorporating Shape Memory Alloys (SMAs). SSDI has proven effective in managing energy dissipation during electronic switching transitions, mitigating voltage spikes and reducing electromagnetic interference. However, the method consumes a substantial amount of energy. In this study, we propose a hybrid approach that combines the benefits of SSDI with the unique properties of SMAs to achieve improved vibration control. The integration of SMAs introduces a semi-passive element to the system, exploiting the inherent properties of these alloys to undergo reversible shape changes in response to external stimuli. By strategically placing SMAs within the vibration-prone components of the system, we aim to harness the mechanical forces generated during shape recovery to counteract and dampen vibrations. This innovative approach aims to capitalize on the efficient energy conversion capabilities of SMAs, thus minimizing the additional energy consumption associated with traditional semi-passive methods. The research involves theoretical modeling and simulation studies to assess the effectiveness of the proposed technique. Preliminary results demonstrate a significant reduction in vibration amplitudes and enhanced damping capabilities, validating the potential of the integrated SSDI-SMA system. The findings of this study not only contribute to advancements in vibration control methodologies but also provide insights into the broader applications of SMA-enhanced semi-passive techniques in improving the overall performance and efficiency of systems.