Voltage Variations Research Articles

Role of thermal and electric fields in the failure mechanism of metallized film capacitors: insights from molecular chain dynamics within polymer dielectrics

Abstract The seamless integration of modern power systems and renewable energy sources heavily relies on advanced power electronics technologies, with metallized film capacitors (MFCs) playing a pivotal role in this ecosystem. Ensuring the operational efficiency and safety of these systems necessitates a thorough understanding of MFC failure mechanisms. In this study, molecular dynamics and density functional theory are employed to analyze the microscopic parameters governing MFC failure characteristics across a temperature spectrum of 25 °C–105 °C. The investigation is geared toward theoretically assessing MFC failure mechanisms under varying voltage ramp rates. Our findings highlight temperature as the primary influencer of failure characteristics at slower ramp rates (50 and 75 V s−1), where the interplay between carrier transport and intermolecular interaction energy dictates the trend of capacitor failure voltage—a pattern of initial increase followed by decrease with temperature. Conversely, higher voltage ramp rates accentuate the significance of the electric field. At a rapid ramp rate of 900 V s−1, the dominance of the electric field mitigates the impact of temperature, resulting in minimal variation in failure voltage across temperatures. Moreover, under intense electric fields, the reduction in free volume within the polypropylene unit exhibits a rapid decline, significantly constraining the mobility of molecular chains. Consequently, certain segments of these molecular chains exhibit localized alignment and directional movement. The rise in molecular polarity and reduction in energy gap contribute to substantially lower failure voltages compared to slower ramp rates. This study offers robust theoretical insights into comprehending MFC failure characteristics, thereby ensuring their reliable operation in demanding environments.

Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose

Background Computed tomography angiography (CTA) provides significant information on image quality in vascular imaging, thus offering high-resolution images despite having the disadvantages of increased radiation doses and contrast agent-related side effects. The deep-learning image reconstruction strategies were used to quantitatively evaluate the enhanced contrast-to-noise ratio (CNR) and the dose reduction effect of subtracted images. Objective This study aimed to elucidate a comprehensive understanding of the quantitative image quality features of the conventional filtered back projection (FBP) and the advanced intelligent clear-IQ engine (AiCE), a deep learning reconstruction technique. The comparison was made in subtracted images with variable concentrations of contrast agents at variable tube currents and voltages, enhancing our knowledge of these two techniques. Methods Data were obtained using a state-of-the-art 320-detector CT scanner. Image reconstruction was performed using FBP and AiCE with various intensities. The image quality evaluation was based on eight iodine concentrations in the phantom setup. The efficiency of AiCE relative to FBP was assessed by computing parameters including the root mean square error (RMSE), dose-dependent CNR, and potential dose reduction. Results The results showed that elevated concentrations of iodine and increased tube currents improved AiCE performance regarding CNR enhancement compared to FBP. AiCE also demonstrated a potential dose reduction ranging from 13.7 to 81.9% compared to FBP, suggesting a significant reduction in radiation exposure while maintaining image quality. Conclusions The employment of deep learning image reconstruction with AiCE presented a significant improvement in CNR and potential dose reduction in CT angiography. This study highlights the potential of AiCE to improve vascular image quality and decrease radiation exposure risk, thereby improving diagnostic precision and patient care in vascular imaging practices.

Fault Resistance and Adaptive Analysis of Induction Motors under Voltage Variations in Industrial Applications

Fault Resistance and Adaptive Analysis of Induction Motors under Voltage Variations in Industrial Applications

Realizing multi-level phase-change storage by monatomic antimony

With the growing need for extensive data storage, enhancing the storage density of nonvolatile memory technologies presents a significant challenge for commercial applications. This study explores the use of monatomic antimony (Sb) in multi-level phase-change storage, leveraging its thickness-dependent crystallization behavior. We optimized nanoscale Sb films capped with a 4-nm SiO2 layer, which exhibit excellent amorphous thermal stability. The crystallization temperature ranges from 165 to 245 °C as the film thickness decreases from 5 to 3 nm. These optimized films were then assembled into a multilayer structure to achieve multi-level phase-change storage. A typical multilayer film consisting of three Sb layers was fabricated as phase-change random access memory (PCRAM), demonstrating four distinct resistance states with a large on/off ratio (∼102) and significant variation in operation voltage (∼0.5 V). This rapid, reversible, and low-energy multi-level storage was achieved using an electrical pulse as short as 20 ns at low voltages of 1.0, 2.1, 3.0, and 3.6 V for the first, second, and third SET operation, and RESET operation, respectively. The multi-level storage capability, enabled by segregation-free Sb with enhanced thermal stability through nano-confinement effects, offers a promising pathway toward high-density PCRAM suitable for large-scale neuromorphic computing.

Adaptive Approach for Power Oscillation Damping using STATCOM

In this paper we had described the designing of a Power Oscillation Damping (POD) controller for a Static Synchronous Compensator (STATCOM) equipped with Energy Storage System (ESS). Various adaptive techniques like fuzzy logic controller, PID controller, algorithm, etc. can be employed to achieve this allowing a fast and adaptive approximation of the low-frequency electromechanical oscillations from locally measured signals during power system disturbances. We developed an approach using variable DC voltage control and constant modulation index method; which are effective in increasing the damping of the system at the concerned frequencies and in case of system parameter uncertainties. A control policy that optimizes active and reactive power inoculation at various connection points of the STATCOM is derived using the simplified model in MATLAB Simulink using impower system toolbox. Signal analysis of the dynamic performance of the proposed control strategy is carried out. To verify the effectiveness of the proposed control method, we carried out simulations and found that the approach provides oscillation damping irrespective of the connection point of the device and also in the presence of system parameter uncertainties.

Optimizing inductance for minimizing reactive power in single-phase-shift modulated dual active bridge converters under variable input voltage

Optimizing inductance for minimizing reactive power in single-phase-shift modulated dual active bridge converters under variable input voltage

Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data

The inconsistency of battery voltages in all-electric ships is a significant issue for electric vehicle battery systems, leading to numerous safety concerns during vessel operation. Therefore, timely fault diagnosis and accurate fault prediction are crucial for the safe operation of ships. This study examines the fault alarm system of marine battery management systems in conjunction with the unique operating conditions of ships, focusing on the system’s latency. To facilitate prompt fault detection, a fault diagnosis method based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed, utilizing the voltage data of battery clusters. Results indicate that the DBSCAN clustering algorithm demonstrates superior effectiveness and accuracy in identifying irregular battery clusters. Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. Experimental findings suggest that this model can effectively predict consistency faults and over-/under-voltage conditions based on battery cluster voltage values and corresponding fault thresholds.

A low-power single ended half-select free 7 T SRAM cell with improved write margin at 32 nm technology node

Abstract A single-ended single port 7T SRAM bit-cell with a process-variation tolerant architecture and half-select resilience at 32 nm CMOS technology node is presented. The proposed 7 T (7TP) SRAM bit-cell integrates a low threshold voltage (LVTH) feedback cutting transistor along with nominal VTH devices. The reliability of the 7TP cell is presented for process, voltage and temperature (PVT) variations to account for local/global variations incurred during CMOS fabrication. 7TP SRAM bit-cell consistently exhibits 6-σ deviation for static noise margin (SNM), Write Margin (WM) and delay using 1000-point Monte Carlo simulations. The 7TP bit-cell shows improved WM, SNM, power consumption and delay compared to other SRAM cell architectures. The 7TP has the lowest normalized WM-1(WM- 0) of 8.5025%(8.5025%) compared to 17.0150%(46.0875%), 36.5800%(36.5800%), 17.0150%(53.5725%), 53.5450%(14.3650%), 11.8925%(11.8925%) for 7Tn, 7Ti, 7Tj, 8T and 11T SRAM cells respectively. The read-1(read-0) and write-1(write-0) power of the 7TP cell is 887.2 nW(10.09 μW) and 1087 nW(26.6 μW) respectively. The write-1 power of 8 T, 9 T and 10 T cells is 112.2%, 113.70%, and 136.11% of 7TP cell respectively. The proposed cell has the second-best HSNM/RSNM value of 0.269 V at VDD = 0.8 V. The area of the proposed cell is 0.104 sq. μm, the least among other SRAM bit-cells.

Experience in the Development of the Sine-Wave Filter for High-Power Variable Voltage Variable Frequency Drive with Medium Voltage Induction Motor

Experience in the Development of the Sine-Wave Filter for High-Power Variable Voltage Variable Frequency Drive with Medium Voltage Induction Motor

High speed and low power hybrid adder using 22 nm SG-TG FinFET

Abstract This paper demonstrates a new 22-T (transistor) hybrid adder for low-power applications using 22 nm shorted gate (SG) tri-gate (TG) FinFET technology. Large conducting channels and increased gate control make FinFET suitable for high-speed low-power computing circuits. The proposed hybrid adder is designed using pass transistor and CMOS logic. Also, it adopts the state-of-the-art techniques of dynamic leakage balancing on the ground path and stacking ground transistors that reduce parasitic effects and increase the speed of the logic transitions compared to traditional designs. Extensive simulations were executed to characterize for PDP (Power Delay Product), power dissipation, and delay of the hybrid adder circuit. From the Monte Carlo analysis, the robustness and efficiency of the circuit are also observable for minimum variation in power and delay due to process voltage and temperature (PVT) variations. Besides, concerning the supply variability and temperature, the circuit shows excellent stability by keeping the power consumption and delay performance consistent. The proposed 22-T hybrid adder using 22 nm SG-TG FinFET are very well-suited in low power and high-speed processors showing highly stable behaviour with reduced process variability.

Optimizing power sharing accuracy in low voltage DC microgrids considering mismatched line resistances

The main difficulties facing the operation of parallel converters in DC microgrids (DCMGs) are load sharing, circulation current, and bus voltage regulation. A droop controller is commonly used to control current sharing among parallel DC–DC converters due to its simplicity. However, the values of droop parameters impact both bus voltage regulation and the error in current sharing among converters. This study introduces an adaptive and straightforward droop control mechanism designed to estimate droop parameters. The algorithm aims to enhance both bus voltage regulation and load sharing performance within DCMGs. Additionally, to mitigate bus voltage deviation in DCMGs, the second loop shifts the droop lines to maintain voltage regulation at rated values. The proposed method has been compared with the conventional droop controller under variable input voltage and load resistance conditions. Simulation results demonstrate that the presented method outperforms the traditional control approach, achieving superior performance.

Detection of internal short circuit in lithium-ion batteries based on electrothermal coupling model

Internal short circuit (ISC) is the main cause of thermal runaway (TR) in lithium-ion batteries, and early detection of ISC is crucial to improve battery safety. This paper introduces a method for detecting ISC and classifying fault severity by analyzing the variations in voltage and surface temperature during battery operation. Firstly, the electrothermal coupling model (ETCM) of the battery was constructed, and the Pearson correlation coefficient (PCC) was used to identify the ISC of the battery through the real-time collected voltage and temperature sensor data When the battery was working. Then the battery model with ISC is updated by using equivalent ISC resistance. The battery status estimation is achieved by integrating the extended Kalman filter (EFK) and sliding mode observer (SMO). Finally, the fault grade is classified according to the state difference between the battery with ISC and normal battery. The batteries with different degrees of ISC were installed in battery packs and verified by the StarSim hardware-in-the-loop (HIL) experiment. The findings suggest that the proposed approach facilitates rapid identification of ISC in the battery pack and enables categorization of fault severity based on the state estimation outcomes derived from analyzing the battery with ISC.

Impact of voltage variation on topography, hydrophobicity, and corrosion behavior of the CeO2-TiO2 nanocomposite coating on AM60B Mg alloy prepared by plasma electrolyte oxidation

Abstract A superhydrophobic oxide layer on AM60B magnesium alloy was created by using a plasma electrolytic oxidation (PEO) technique and subsequent surface modification. PEO was applied in an electrolyte with/without CeO2-TiO2 nanoparticles (NPs) by two different voltages of 350 and 450 V. Atomic force microscopy (AFM) and field emission scanning electron microscopy (FE-SEM) were used to evaluate the roughness and morphology of the surface. Water contact angles on the surface were measured to investigate the surface wettability. Potentiodynamic polarization tests in a 3.5 wt.% NaCl solution were used to assess the corrosion resistance of the coating. Also, the FE-SEM images showed that increasing applied voltage, decreases the numbers and size of the pores on the surface. Results showed that applying PEO, increased the corrosion resistance of the bare AM60B because corrosion rate (C.R.) decreased from 1.523805 (bare AM60B) to 1.432107 mpy (PEO-450 V). Furthermore, using CeO2-TiO2 NPs effectively enhanced the corrosion resistance of the bare AM60B by decreasing the corrosion rate from 1.523805 to 1.286469 mpy.

A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System

This paper examines the formulation and implementation of a neuro-controller for the excitation system of synchronous generators in a Single-Machine Infinite Bus (SMIB) power system. The SMIB model is employed as a fundamental model of a power system, thereby facilitating the assessment and comparison of disparate control strategies with the objective of enhancing system stability. The goal of this study is to enhance the stability of the SMIB power system through the implementation of an Artificial Neural Network (ANN) neuro-controller, providing a comparison of its performance to that of a Power System Stabilizer (PSS) and a Proportional-Integral-Derivative (PID) controller. The proposed neuro-controller will be integrated into the generator's excitation system and will be designed to regulate the excitation voltage in response to fluctuations in the system's operational parameters. To this end, an ANN is calibrated to account for the singularity of the generator's excitation level and terminal voltage. The Levenberg-Marquardt algorithm is employed to ascertain the optimal weight coefficients for the ANN. To assess the performance of the neuro-controller, simulations were conducted using MATLAB/Simulink. The simulations encompass a comprehensive range of operational scenarios, including diverse disturbances and alterations in the reference voltage level. Subsequently, the neuro-controller's outputs are evaluated in comparison to the PSS and PID controllers, as these are the prevailing controllers used to enhance voltage regulation and transient stability in power systems. This paper presents the results of an analysis of the neuro-controller's impact on the system's robustness, voltage variation amplitude, and generator dynamic performance during faults. Simulation results demonstrate that the application of an ANN-based neuro-controller yields superior outcomes in voltage regulation and transient stability compared to the conventional controllers PSS and PID. Furthermore, the neuro-controller is distinguished by accelerated response times and enhanced precision in voltage level regulation. The neuro-controller represents a superior approach to the control of a power system, particularly in the context of SMIB, which would ultimately result in enhanced performance and stability.

Design of Flash analog-to-digital converter based on MoS2 FET

In this paper, we leverage the advantages of MoS2 FETs to design a 4-bit Flash ADC that achieves a reduced active area and dynamic power. The proposed ADC employs a threshold inverter quantization (TIQ) technique to eliminate static power dissipation. A SPICE-compatible charge-based model for MoS2 FETs is used to simulate the proposed ADC. The high ON/OFF current ratio and nanoscale size of MoS2 FETs contribute to a smaller ADC active area and lower dynamic power compared to other technologies. Simulation results indicate a differential nonlinearity (DNL) of [−0.18, 0.12]LSB and an integral nonlinearity (INL) of [−0.32, 0.24]LSB meeting the requirements for 4-bit resolution at a 2 V operating voltage. While the FFT spectrum analysis reveals a signal-to-noise ratio (SNR) of 29.37 dB, the effective resolution bandwidth (ERBW) of 2.2 GHz is obtained, highlighting the ADC’s performance in high-speed applications. Additionally, the low ADC active area of 3000 μm2 makes it suitable for very large-scale integration (VLSI) circuits. The proposed method is sensitive to variations in process, temperature, and power supply voltage, and their effects on ADC performance are also examined.

A distributed double-layer control algorithm for medium voltage regulation and state of charge consensus of autonomous battery energy storage systems in distribution networks

The increasing integration of Distributed Generation (DG) based on Renewable Energy Sources (RES) in traditional distribution systems necessitates the adoption of smart grid strategies. These strategies rely heavily on energy storage actuation to manage the inherent variability of RES and ensure autonomous operation. This article presents a hierarchical digital control strategy for managing distribution power systems, utilizing Battery Energy Storage Systems (BESS) to regulate voltage amplitude and enhance overall behavior for efficient energy management. At the primary control level, operating at a minute-scale actuation rate, an integral voltage regulation loop determines the BESS power injection or consumption, refined by a fuzzy conditioning of signals that accounts for the state of charge and operational modes of each device. Laplacian and random switching secondary consensus control strategies, operating at a slower rate, ensure coordinated action among individual units. The convergence of these strategies is analytically validated using Lyapunov’s theory under idealized conditions. Extensive dynamic simulations over a 24-hour period demonstrate the proposed strategy’s effectiveness in improving power quality and coordinating voltage regulation, particularly in terms of enhancing the power factor and reducing the standard deviation of voltage and power variables over time in a coordinated manner.

Speed control analysis of voltage source inverter fed brushless DC motor

The brushless DC (BLDC) motor requires to be controlled at the preferred speed in order to operate. A brushless DC motor's speed can be adjusted by adjusting the input voltage. In general, speed increases with voltage. The application of a Luo converter is made to satisfy the load demand, get rid of output voltage ripples, and reduce parasitic effects. The magnitude of stator input voltage to BLDC motor is controlled through the pulses applied by ATMEGA 328P micro controller to voltage source Inverter which in turn controls the magnitude of speed of BLDC motor. The position of the brushless DC (BLDC) motor is continually monitored by infrared sensors, which are then processed by a PIC16F872 microcontroller to produce the necessary pulses for BLDC motor speed regulation. The BLDC motor speed can be regulated by the pulses applied to voltage source inverter through the IR sensors placed at the motor. The outcomes of controlling the speed of a BLDC motor using voltage variation values have been shown.

Hetero dielectric based dual material gate AlGaN channel MISHEMT for enhanced electrical characteristics

Herein, we report an AlGaN channel metal insulator semiconductor high electron mobility transistor (MISHEMT) in dual material gate (DMG) architecture with two different dielectrics as gate insulator for enhancing the DC performance of the device. The use of a high-k dielectric material as gate insulator below gate metal 1 and a low-k dielectric below gate metal 2 results in significant threshold voltage variation along the channel. This contributes to improved average carrier velocity which in turn results in better current drive. The proposed device exhibits a peak current of 162 mA/mm at zero gate bias, whereas, the DMG and single material gate (SMG) AlGaN channel HEMTs offer currents of 137 mA/mm and 125 mA/mm respectively. The peak transconductances are about 24.66 mS/mm, 23.68 mS/mm and 22.71 mS/mm respectively for the proposed device, DMG and SMG HEMTs. The proposed device reduces the OFF current by an order of 106 compared to that of DMG HEMT.

Understanding voltage variation in interface corrosion reactions: A theoretical approach based on PDM and DFT

Understanding voltage variation in interface corrosion reactions: A theoretical approach based on PDM and DFT

Fabrication and testing of PVDF-Fish scales based sustainable piezoelectric impact sensor

Structural health monitoring is essential to modern infrastructure management, ensuring safety, optimizing costs, and enhancing structures' overall performance and reliability. Leftover fish scales (FS) in varying quantities (5, 10, 15, and 20wt.%) are reinforced into a polyvinylidene fluoride (PVDF) matrix to develop highly flexible piezoelectric composites, which are then assessed as impact sensors. The role of FS loading on the mechanical and dielectric behavior of PVDF is also investigated. PVDF with 10% FS loading showed maximum Ultimate tensile strength (UTS) and Elastic modulus (E) of 37MPa and 1192MPa, respectively. A 56% and 67% enhancement in UTS and E values over pure PVDF matrix is recorded. Piezoelectric nanogenerators (PENGs) are then fabricated to check the piezo response of composites under various loading conditions using a human hand (finger tapping, palm tapping, film twisting, and wrist pressing). The device with a maximum piezo response is further assessed for potential impact sensing under different impact loading conditions. The recorded results are analyzed statistically using the Weibull distribution approach to cater to the o/p voltage variations for each test load. The potential for sustainability and circular economies is significantly expanded with a cost-effective, highly sensitive piezoelectric impact sensor crafted from FS.