Voltage Variations Research Articles

COOLING PERFORMANCE POTENTIAL OF TEC1-12710 AND TEC1-12715 SR THERMOELECTRIC MODULES WITH HOT SIDE COOLING OPTIMIZATION METHODS USING SEVERAL TYPES OF HEAT EXCHANGERS

Thermoelectric module is a mature technology that have been used in cooling applications such as special product storage cabins and cooling electronic products. Compared to the conventional technology (vapor compression systems), thermoelectric modules offer many advantages. However, in terms of cooling performance, particularly for large cooling capacities (>100Watt), thermoelectric modules still lag the conventional technology. One method that can be used to improve the cooling performance of thermoelectric modules is cooling the hot side of the module using a heat exchanger. In this study, experiments will be carried out on the TEC1-12710 and TEC1-12715 SR modules which alternately combined with five types of heat exchangers on the hot side of the module, connected by thermal paste. The types of heat exchangers used are Square HE, Round HE, Two-Pipe HE, Four-Pipe HE, and Liquid-Cooler HE. The experiments are carried out with operating voltage variations of 12V, 9V and 6V for each thermoelectric module. The data analysis results found that the best COP value was obtained by the TEC1-12715 SR thermoelectric module with the Round type Heat Exchanger of 0.767 at a voltage of 6V. For the TEC1-12710 thermoelectric module, the highest COP value was also obtained when the module was paired with a Round type Heat Exchanger at a voltage of 6V, with a value of 0.620. Additionally, the highest heat absorption value for both thermoelectric modules was obtained at a voltage of 12V using the Round type heat exchanger, namely 19.61 Watts (TEC1-12710) and 26.98 Watts (TEC1-12715 SR), respectively.

Effect of process parameters on the growth and microstructure of anodic films on 6061 aluminum alloy in alkaline electrolytes

This study investigates the anodizing of 6061 aluminum alloy using an alkaline solution composed of sodium hydroxide (NaOH), sodium tetraborate (Na2B4O7) and potassium pyrophosphate (K4P2O7). The effects of the electrolyte compositions as well as temperature and voltage on the growth and microstructure of anodic films were investigated. Results showed that all these parameters have effects on the current density of the anodizing process and thickness of the anodic films. The current density increases with process parameters except for Na2B4O7, while as the process parameters increase, the anodic film thickness initially increases and then decreases. The morphology of the anodic film is notably affected by variables NaOH, Na2B4O7, temperature, and voltage. Increasing NaOH concentration and temperature resulted in a decrease in surface uniformity, while Na2B4O7 exhibited an opposite effect. Voltage significantly affected the diameter of the anodic film's pores, resulting in larger pores at higher voltages, which leading to poorer dyeing effect for the anodized samples.

Improved LSTM-Squeeze Net Architecture for brain activity detection using EEG with improved feature set

Brain activity recognition (BAR) has emerged as most intriguing fields of research in recent days. It poses the ability to transform an extensive number of appliances, together with ICU surveillance, appliance control for the disabled and aged, and the detection of neural diseases. EEG data are the most common representation of brain activity, as they capture the voltage variations of neurons in the brain using non-invasive electrodes implanted on the scalp. Deep learning models play a major role in recognizing brain activity. This work uses EEG inputs to present a new hybrid model-based BAR. Adaptive least mean square (ALMS) is first used to filter the input signal for EEG signals. Next, improved CSP features, morphological features, temporal domain features, and wavelet packet decomposition (WPD) features are extracted. Appropriate characteristics are chosen following feature extraction using Improved Mutual Information (IMI). Then, using an enhanced LSTM (ILSTM)-Squeeze net model, brain actions are detected or recognized. The highest accuracy at LP = 90 is around 0.961. In contrast, the precisions of 0.7385, 0.667, 0.7516, 0.771, 0.686, 0.7124, and 0.7189 were obtained by Dense Net, Mobile Net, Squeeze Net, Res Net, ANN, LSTM, and RNN. The evaluation done proves the efficiency of the proposed model over the other conventional models.

Development and investigation of DMDG-MOSFET biosensor for charged biomolecule detection

Abstract The paper explores the analog and sensitivity parameter of a n-channel gate stack Dual Material Double Gate (DMDG) MOSFET biosensor, specifically focusing on its response to a wide range of charged biomolecule introduced into its cavity region. This novel structure offers improved sensitivity and selectivity due to its ability to modulate the threshold voltage and control the electrostatic environment more precisely compared to conventional MOSFET-based biosensors. The analysis includes a thorough examination of the surface potential, electric field, transconductance, and threshold voltage variations influenced by the presence of charged biomolecules. By applying a parabolic-potential technique to solve the 2-D Poisson's equation, the expression for surface potential can be found. The minimal surface potential model is used to calculate the threshold voltage. Using SILVACO ATLAS, the simulation findings suggest that the proposed gate stack DMDG-MOSFET structure demonstrates sensitivity of 0.123 V and 0.607 V for neutral and charged biomolecules respectively emphasizing the impact of gate material engineering on the biosensor's performance.

Robust Generalized Super‐Twisting Sliding Mode Control Design for Optimal Performance in Three‐Phase Grid‐Connected Photovoltaic Systems

ABSTRACTThis article introduces a third‐order super‐twisting sliding mode control (Gen‐STSMC) algorithm designed for the secure operation of a grid‐connected photovoltaic (PV) system. The improved method changes the traditional super‐twisting sliding mode control (STSMC) technique by incorporating both a linear term and a higher power term to reduce convergence time and chattering effects. This strategy generates the optimal control signals to regulate the output power of the PV system by compensating the state‐dependent uncertainties using the linear and growing term. The controller's stability analysis is conducted to demonstrate its rapid convergence compared to PI and conventional STSMC scheme. A comparative simulation study against PI and STSMC is conducted to showcase the controller's rapid convergence and robust performance in various grid‐connected PV system scenarios. Experimental tests are also performed to validate the controller's effectiveness in maintaining stable power generation in the presence of environmental fluctuations and DC link voltage variations.

Tailoring threshold voltage of R2R printed SWCNT thin film transistors for realizing 4 bit ALU

Despite the roll-to-roll (R2R) gravure printing method emerging as an alternative sustainable technology for fabricating logic circuits based on p- and n-types of single-walled carbon nanotube thin film transistors (p,n-SWCNT-TFTs), the wide variation of large threshold voltage (Vth > ~8) in the R2R printed p,n-SWCNT-TFTs prevents the integration of complementary logic circuit. Here, the Vth variation of the p,n-SWCNT-TFTs was narrowed down by developing a method of using the first gravure roll with the minimized superposition error (< ±40 µm) of engraved registration marks and implementing the R2R doping process for tailoring the Vth using polymer-based p- and n-doping inks. Through those two methods, the R2R printed the p,n-SWCNT-TFTs was tailored to shift Vth to near ±2.7 V and reduce Vth variation to ±1.6 V while the noise margin was improved by 24% so that a large number of R2R printed logic gates could be integrated with clear logic levels at ±10 V of operation voltage. Based on the tailored p,n-SWCNT-TFTs, a fully R2R printed 4-bit arithmetic and logic unit was successfully demonstrated by integrating 156 p,n-SWCNT-TFTs.

Investigation of the Electrochemical Methylene Orange Effluent Degradation Using Graphite Battery Waste and Seawater

Electrochemical degradation using seawater and graphite electrodes from waste batteries for methylene orange (MO) dye degradation has been successfully carried out. Electrode characterization, electrolyte calibration, and dye degradation effectiveness with various voltages, pH, kinetics, and degradation reaction mechanisms were observed. Characterization showed a characteristic 2θ peak by X-ray diffractometer (XRD) at 26.5 °, and scanning electron microscopy-energy dispersive X-ray spectrometer (SEM-EDX) showed the morphology of hollow grains with carbon (C) and oxygen (O) composition. In addition, scanning potentiometry showed the initiation of hypochlorite compound formation and MO degradation. Further study of degradation effectiveness showed optimal results at 3 volts and above voltages. Hypochlorous acid (HOCl) and hypochlorite ions (OCl-) from seawater are very effective oxidizing agents in acidic conditions compared to other pH conditions. Pseudo-first-order kinetics regulate the electron attack from the hypochlorite oxidizer (OCl-), the hydroxyl groups (•OH) from the seawater electrolysis mechanism with graphite electrode media on the reactive groups, the ring binding on MO, and the degradation. This indicates that the graphite electrode system used to reuse waste batteries and seawater has the potential to degrade waste dyes in aquatic environments. HIGHLIGHTS Electrochemical system with graphite electrode battery waste and seawater electrolyte. Characterization with XRD, SEM-EDX, spectrometry, and potentiometry. Analysis of the effect of voltage and pH treatment variation. Kinetic and degradation mechanism of methylene orange. GRAPHICAL ABSTRACT

The Development Of Charging System Infrastructure For Electric Vehicles

The increasing use of electric vehicles (EV) requires adequate and efficient charging system infrastructure. One of the main things is the electric vehicle (EV) battery charging system. This research aims to develop a charging system infrastructure for EVs using a DC to DC converter. The system is designed to ensure safety, efficiency and compatibility with a wide range of EVs. The system is also capable of producing voltage from 12 volts to more than 60 volts, which is enough to charge electric vehicle batteries. Correlation analysis simulations with MATLAB and experimental testing were carried out to evaluate the performance of the charging system. This circuit can charge batteries with voltage variations from 12 volts to 60 volts which are widely used in electric vehicles. The results of correlation analysis show that there is a correlation between charging current and charging time. Where the greater the charging current, the faster the charging time. The temperature of the battery when charging must also be paid attention to so that overheating does not occur which can damage the battery cells.

An ANN-Based Method for On-Load Tap Changer Control in LV Networks with a Large Share of Photovoltaics—Comparative Analysis

The paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of the load compensation (LC) function with settings determined via artificial neural network (ANN) algorithms. The proposed method was compared with other selected local methods recommended in European regulations, in particular with those currently required by Polish distribution system operators (DSOs). Comparative studies were performed using the model of the 116-bus IEEE test network, taking into account the unbalance in the network and the voltage variation on the medium voltage (MV) side.

A Comprehensive Study of Voltage Swell and Sag in Power Distribution Systems: Characteristics, Causes, Effects, and Mitigation Strategies

Voltage variations, such as swells and sags, are prevalent disturbances in power distribution systems, posing significant challenges to system reliability and equipment performance. This research paper provides a comprehensive study of voltage swell and sag phenomena, encompassing their characteristics, causes, effects, mitigation strategies, and case studies. The study explores the magnitude, duration, and frequency of voltage variations, delving into the underlying factors contributing to their occurrence, including switching operations, faults, and load variations. Moreover, the paper examines the detrimental effects of voltage variations on electrical equipment and industrial processes, highlighting the importance of understanding and mitigating these disturbances. Various mitigation techniques, such as voltage regulation, uninterruptible power supplies (UPS), and dynamic voltage restorers (DVRs), are evaluated for their effectiveness in minimizing the impact of voltage variations. Additionally, the paper explores the use of PWM-based Distribution Static Synchronous Compensator (DSTATCOM) as a promising solution for mitigating voltage swell and sag issues, discussing principles, design considerations, control strategies, and performance evaluation. Through case studies, simulation results, and modeling approaches using MATLAB/Simulink, the research paper offers valuable insights into the complex landscape of voltage swell and sag in power distribution systems, equipping researchers and practitioners with knowledge to address these challenges effectively.

Optimization Conditions for High-Power AlGaN/InGaN/GaN/AlGaN High-Electron-Mobility Transistor Grown on SiC Substrate.

In this study, we aimed to propose an optimal structure for an AlGaN/InGaN/GaN/AlGaN/SiC HEMT by investigating how the breakdown voltage varies with the thickness and composition of the InGaN layer. The breakdown voltage was shown to be highly dependent on the In composition. Specifically, as the In composition increased, the breakdown voltage rapidly increased, but it exhibited saturation when the In composition exceeded 0.06. Therefore, it is desirable to maintain the In composition at or above 0.06. The variation in breakdown voltage due to thickness was relatively small compared to the variation caused by In composition. While the breakdown voltage remained nearly constant with increasing thickness, it began to decrease when the thickness exceeded 10 nm. Hence, the thickness should be kept below 10 nm. Additionally, as the In composition increased, the subthreshold swing (SS) also increased, but the drain current value was shown to increase. On the other hand, it was observed that the SS value in the transfer characteristics and the current-voltage characteristics were almost unaffected by the thickness of the InGaN layer.

Efficient voltage control strategy: observability design for multistage DC–DC buck converter

This paper emphasizes the significance of implementing an effective control system to enhance the performance of a three-stage DC–DC buck converter (TDDC). Herein, we present the development of an observer controller (OC) for TDDC, where average modeling of the DC–DC converter was employed alongside an observer algorithm for the OC. The primary focus is on the adoption of an observation topology aimed at enhancing system responsiveness under various conditions, including variable input voltages, reference voltage changes, load fluctuations, integration with photovoltaics, and battery charging. This approach ensures improved stability and performance, addressing the dynamic challenges inherent in such systems. A comparative analysis was conducted on two distinct control topologies for the proposed TDDC system, namely proportional–integral (PI) and advanced OC. Simulink software was used to model and simulate the proposed system. The results demonstrate lower rising and settling times of the TDDC for the OC and PI implementations, respectively. Additionally, the system with the OC eliminates 20% of the overshoot, while the system with OC minimizes the output voltage oscillations from 13% to 2% compared to the PI system. The OC was integrated into the TDDC to enhance system stability and improve the control loops, particularly the third loop. These improvements make the TDDC with OC suitable for application to renewable energy systems, microgrids, telecommunication networks, and electric vehicles, where precise control and stability are essential.

Integral Sliding Mode‐Composite Nonlinear Feedback Control Strategy for Microgrid Inverter Systems

To enhance the dynamic performance and robustness of the voltage control system of islanded microgrid inverters, a new control strategy combining integral sliding mode (ISM) control and composite nonlinear feedback (CNF) control is proposed. In ISM control, firstly, a new reaching law is designed to improve movement quality in the reaching phase by improving the power term and introducing the inverse tangent function. Then, to improve disturbance observation accuracy, a variable gain extended state observer is designed by improving the regulation mechanism of the state variables according to deviation control. Using the idea of variable damping, linear and nonlinear feedback are combined into CNF control. Specifically, linear feedback provides a small damping ratio for faster system response, while nonlinear feedback increases the damping ratio to improve steady‐state performance. Simulation results show that the integral sliding mode‐composite nonlinear feedback (ISM‐CNF) control strategy cam balances convergence speed and chattering better and achieves higher steady‐state accuracy than conventional strategies. Moreover, ISM‐CNF control has better robustness to reference voltage variations and disturbances caused by sudden load changes. Therefore, the ISM‐CNF control strategy can accomplish voltage control of islanded microgrid inverters quickly and steadily, effectively suppressing system disturbances and enhancing stability and power quality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Intelligent fault diagnosis in power distribution networks using LSTM-DenseNet network

This paper introduces a novel fault diagnosis method that combines DenseNet and Long Short-Term Memory (LSTM) networks. The DenseNet utilizes its unique dense block structure to detect subtle variations in three-phase voltage and zero-sequence current signals. In addition, the Squeeze-and-Excitation (SE) module is introduced in DenseNet. The SE module enhances DenseNet's feature representation by adapting the importance of each channel in the feature map. Furthermore, integrating the LSTM model enables capturing time-domain features of fault signals, enhancing the analysis of waveform changes and trends. These extracted features are subsequently fused in a cascaded manner, leveraging the strengths of both approaches to obtain a more comprehensive information representation. To better explain the capability of feature extraction in each part of the model, t-distributed Stochastic Neighbor Embedding (t-SNE) method is used for visual analysis. The proposed method is evaluated using two distribution network models, namely the 10 kV and IEEE34 networks, in simulation. The verification results indicate that the proposed method achieves exceptionally high accuracy in fault identification for both tested distribution network models, with rates of 99.87 % and 99.82 %, respectively, while also demonstrating robust performance in noisy environments. This performance surpasses that of other related methods, underscoring the enhanced effectiveness of our approach.

Lightweight high-throughput true random number generator based on state switchable ring oscillator

True random number generators (TRNGs) perform an extremely critical role in cryptographic algorithms and security protocols, scientific simulation, industrial testing, privacy protection, and numerous other domains. Nevertheless, modern TRNGs have difficulty striking a reasonable balance between high throughput and low hardware consumption. In this paper, a novel lightweight high-throughput TRNG based on state switchable ring oscillators (SSROs) is proposed. Under the effect of flip-flops that are prone to entering the metastable region, the SSROs randomly switch between oscillatory and buffer states to create jitter and metastability. A feedback strategy is adopted to effectively eliminate the fixed point in the circuit, which further enhances the randomness of the structure. The proposed TRNG is implemented on Xilinx Artix-7 and Kintex-7 FPGAs, with support for automatic routing. It achieves a throughput of up to 400 Mbps while consuming only 16 LUTs and 13 DFFs, showing extremely high resource utilization efficiency. Experimental results show that the output random sequence passes the NIST SP800-22 test, the NIST SP800-90B test, and the AIS-31 test without any post-processing, exhibiting strong robustness against voltage and temperature variations as well as frequency injection attacks.

A Robust Low‐Power Power‐On‐Reset Circuit With Dual Threshold Method

ABSTRACTIn this paper, a robust low‐power power‐on‐reset (POR) circuit with dual threshold method is proposed. The current‐based circuit exhibits good robustness with respect to process, voltage, temperature, and supply ramp variations. In contrast to conventional delay‐based pulse generation approaches, a dual threshold method is introduced to generate POR and brown‐out‐reset (BOR) pulse signals, resulting in ultralow quiescent current and a compact area. Implemented in the 55‐nm CMOS process, the proposed circuit achieves an accurate trigger‐point voltage with a temperature coefficient of 90.4 ppm/°C and a deviation to the ramp time of 2.9% for a range from 100 μs to 1 s. Furthermore, the quiescent current and active area are 0.3 nA and 4834 μm2, respectively, making it particularly suitable for energy harvesting systems.

Image quality enhancement in variable refresh rate LTPO-based AMOLED displays using a gate in panel voltage compensation scheme

This study investigated novel driving scheme for low-temperature polycrystalline silicon and oxide (LTPO) active-matrix organic light-emitting diode (AMOLED) displays under variable refresh rate (VRR) driving conditions with aim of reducing the brightness instability (i.e., flicker and luminance variation) for seamless performance. The mechanisms underlying brightness fluctuations in displays were examined, focusing on frequency-dependent differences in gate-inpanel (GIP) operations. Notably, our study revealed additional factors within the GIP operation that cause LTPO flicker and VRR performance, in addition to previously identified factors of thin-film transistor hysteresis and OLED charging. Consequently, variable GIP voltage driving method that reduced these issues and enhanced display performance was proposed. The proposed method could minimize brightness fluctuations to levels imperceptible to users and facilitate performance improvement through driving options in peripheral circuits without modifying panel design and is applicable across various applications utilizing LTPO OLED technology. Its driving scheme was validated through simulation and measurement of luminance variation during frequency transition using 6.0-inch LTPO-applied AMOLED displays. The experimental results indicated a decrease in luminance difference from 14.2% to 1.5%, particularly at extremely low brightness level of 0.2 nit. The luminance distortion decreased from 7 to less than 1, which is a just noticeable color difference (JNCD).

Complex Reflectivity Modulation Characteristics at Visible Wavelength Using Liquid Crystal on a Metasurface Device

Active metasurface device capable of complex modulation in the visible light range is demonstrated. To realize complex modulation, a liquid crystal (LC) of twisted nematic mode on a plasmonic antenna metasurface, where resonance wavelength is sensitive to its geometry and nearby optical environment, is integrated. For the LC process on the metasurface, the alignment characteristics of the material exposed to the LC on the metasurface are first identified and the polyimide alignment layer is omitted. The LC works in twisted nematic mode to enhance complex modulation characteristics. The complex reflectivity coefficient is measured while varying the voltage applied to both the antenna and metal electrode in the visible light region. Complex reflectivity into a doughnut shape with a small voltage variation of 3.1 V on the antenna electrode and 2.5 V on the metal electrode is successfully modulated. The largest complex modulation is achieved at a wavelength of 660 nm, with phase and reflectivity amplitude modulations of 0–2π radian in a doughnut shape and 0.3 and 0.6, respectively. So far true holographic experience is limited by high‐order overlapping and twin images. Through this approach, a technical method is suggested for overcoming such obstacles and accomplishing advanced holographic display.

Early Warning of Energy Storage Battery Fault Based on Improved Autoformer and Adaptive Threshold

To enhance voltage prediction accuracy in energy storage batteries and address the limitations of fixed threshold warning methods, a fault warning approach based on an improved Autoformer model and adaptive thresholds is proposed. First, a spatiotemporal filtering layer is introduced into the autocorrelation mechanism to analyze the trend features of voltage sequences across different frequency domains. Additionally, an adaptive gating residual connection is used to link the sublayer and current layer output features, which helps to improve the model's adaptive feature selection capability. This innovation enables the development of a robust voltage prediction model based on the enhanced Autoformer. Then, a similarity‐based adaptive threshold, using interval estimation, is employed to rapidly track variations in battery voltage, enabling dynamic adjustment of voltage thresholds. Finally, the proposed method is validated with real voltage data from an operational energy storage station. The experimental results shows that the proposed model has higher accuracy and robustness compared to similar methods. The adaptive threshold can reduce the false alarm rate by ≈18% and issue alarms at three sampling points ahead of the battery management system alarm, improving fault warning accuracy and illustrating that early fault warning is effectively and practically carried out using the method.

AQUILA OPTIMIZED NONLINEAR CONTROL FOR DC-DC BOOST CONVERTER WITH CONSTANT POWER LOAD

Constant power loads (CPL) can be operated by tight-controlled power electronic converters, which have negative impedance and absorb continuous power. Constant power load creates instability in an open-loop system because of its negative incremental impedance. To tackle this problem, a discrete sliding-mode (AQO-DSM) nonlinear control technique has been proposed for a DC-DC boost converter fed CPL based on Aquila optimization. In this paper, the proposed AQO-DSM controllers provide the system stability during a steady state and maintain output voltage regardless of input voltage or CPL variations. In this case, the DSM controller of a DC-DC boost converter's characteristics are adjusted using the Aquila optimization method (AQO). The Lyapunov stability concept is utilized to assess the system's overall stability. Under various system operating conditions, an experimental study and simulation are carried out to validate the proposed controller. The existing methods, such as sliding mode controller (SMC), fuzzy-based SMC (F-SMC), and super twisting algorithm (STA)-based SMC strategies, are compared with a proposed plan to demonstrate the superiority of the proposed AQO-DSMC. Simulated and experimental results have shown that the AQO-DSMC achieves the fastest convergence, the smallest steady-state, settling time under loaded conditions, and consistent chatter reduction compared with all contrasted control methods.