Abstract IGBT includes both merits concerning hidden MOSFET and hidden BJT. The insulated Gate performs like an MOSFET taking control of the whole IGBT just like the role of an igniter or a starter. The scenario does impose an idea that IDS in MOSFET stimulates BJT which is composed of many diodes and equivalently deserves to be placed in the exponential argument of characteristic curve of diodes. However, thermal radiations coming from acceleration and deceleration of electrons or holes do take “kink” effects and block carriers from moving. The heat arises from moving carriers, causes vibrations of lattices, and then resistively reduces the mobility of carriers. Indeed, the final formula, combining MOSFET-BJT-like formula and the kink effects, intimately fits IGBT electrical characteristic curves, and thus serves as a promising algorithm.


Proton exchange membrane fuel cells (PEMFCs) encounter critical efficiency constraints arising from their inherently nonlinear electrochemical and power characteristics under dynamically fluctuating environmental and load conditions, such as abrupt temperature and pressure variations. Conventional MPPT techniques, including perturb & observe (P&O) and conductance increment (INC) algorithms are often plagued by suboptimal convergence dynamics, local minima entrapment, and insufficient real-time adaptability, resulting in significant power loss and system instability. To overcome these limitations, this study introduces an advanced MPPT framework founded on an Interval Type-2 Fuzzy Logic Controller (IT2FLC) optimized through a hybrid Lightning Search Algorithm and Whale Optimization Algorithm (LSA–WOA). The hybrid LSA–WOA meta-optimizer augments the controller's global exploration efficiency, mitigating local entrapment while dynamically tuning six key IT2FLC parameters to ensure optimal response adaptability. The proposed controller integrates a dual-layer inference mechanism that synergistically processes instantaneous power deviation and its rate of change, enabling self-regulated real-time adjustments. A high-fidelity PEMFC circuit model is developed to simulate internal voltage dynamics across a broad operational envelope temperature (273–400 K) and pressure (1–5 atm) conditions, with power regulation achieved through a Zeta DC-DC converter. The proposed method is rigorously validated under three test scenarios: steady-state conditions (343 K, 1 atm), rapid temperature fluctuations, and abrupt pressure changes. A comparative simulation study was conducted to evaluate the performance of the proposed method against several benchmark controllers, including FL, ANFIS, PSO, and GJOA-PI-PD. The results confirm its superiority, demonstrating faster transient responses and enhanced steady-state stability. Under nominal conditions, the proposed MPPT achieves 99.98% tracking efficiency with a rise time of 0.0801 s, a 5% settling time of 0.0818 s, and a residual steady-state error of merely 0.1010 W. Under dynamic perturbations, efficiency attains 99.99% with minimal oscillatory behavior and ultrafast convergence, demonstrating exceptional robustness. This work establishes a substantial advancement in PEMFC MPPT control by fusing the uncertainty-handling resilience of interval type-2 fuzzy logic with the global optimization proficiency of LSA–WOA, thereby enhancing energy extraction, control stability, and reliability in real-world renewable energy systems subject to stochastic environmental and load variations.

Revealing the coupling of internal species and external voltage distribution of PEM fuel cell via a full-scale non-isothermal multiphase model

The rapid increase in retired lithium-ion batteries (LIBs) from electric vehicles (EVs) highlights the urgent need for accurate and automated end-of-life (EOL) assessment. This study proposes an AI-integrated smart grading system that combines hardware diagnostics and deep learning-based evaluation to classify the residual usability of retired batteries. The system incorporates a bidirectional charger/discharger, a CAN-enabled battery management system (BMS), and a GUI-based human–machine interface (HMI) for synchronized real-time data acquisition and control. Four diagnostic indicators—State of Health (SOH), Direct Current Internal Resistance (DCIR), temperature deviation, and voltage deviation—are processed through a deep neural network (DNN) that outputs categorical grades (A: reusable, B: repurposable, C: recyclable). Experimental validation shows that the proposed AI-assisted model improves grading accuracy by 18% and reduces total testing time by 30% compared to rule-based methods. The integration of adaptive correction models further enhances robustness under varying thermal and aging conditions. Overall, this system provides a scalable framework for automated, explainable, and sustainable battery reuse and recycling, contributing to the circular economy of energy storage.

ABSTRACT An Advanced Heffron Phillips Model (AHPM) has been developed to address the issues brought about by Low-Frequency Oscillations (LFO) in the Power System. It is based on a higher-order Synchronous Machine (SM) model 1.1. The internal voltage’s d and q-axis components’ dynamics are included in the AHPM. This is a detailed model with improved mathematical modelling and a smaller number of assumptions for stability studies. The Power System Stabilizer (PSS) is tuned using the Enhanced Snake Optimization Algorithm (ESOA), a novel meta-heuristic algorithm that draws inspiration from nature. With AHPM, the highest damping ratio is (0.9750), and the settling time for various parameters is less than 2.0 seconds. LFO reduces the power system’s ability to transfer power and interferes with regular operation. Enhanced damping torque provided by AHPM with PSS to the system leads to dampening LFO, improvements in stability and power transfer capacity of the system, and hence the achievement of Sustainable Development Goal (SDG) -7. It helps prevent financial losses caused by disturbances in the power system. This illustrates the ESOA-based AHPM’s efficiency and economy. The AHPM ensures the power system operates in a trustworthy, safe, and secure manner, which is essential for overall development.

To address challenges such as internal power balance, voltage stability, and hydrogen storage tank capacity in photovoltaic-storage DC microgrid systems, this paper proposes a hierarchical control strategy that accounts for varying power command demands under different operating conditions. A simulation model of the PV-storage-hydrogen DC microgrid is constructed with the goal of enhancing system stability and achieving smooth mode transitions. On the one hand, the hierarchical control coordinates between the command layer and power demand layer using bus voltage as a key indicator to ensure system stability. On the other hand, the system operating conditions are categorized into eight distinct modes. A multi-power coordinated control strategy is then employed to maintain stable operation, enable rapid and smooth transitions between modes, and keep the hydrogen storage tank capacity within acceptable limits. The proposed model is implemented in MATLAB/Simulink and tested under various scenarios, including sudden load changes, fuel cell operation, and stochastic photovoltaic power fluctuations. Comparative analysis of system overshoot under different control strategies demonstrates that the proposed approach significantly enhances voltage stability. Simulation results confirm the effectiveness and adaptability of the proposed model and control strategy.

In this study, we implemented a compact and cost-efficient linear LED driver for low-power lighting applications. The proposed driver supports both pulse-width modulation and analog dimming to control brightness flexibly. The integrated circuit is implemented using a TSMC 0.5-µm high-voltage mixed-signal LDMOS CMOS process, and key circuit blocks are described. We also fabricated a prototype chip to validate the design. Our experimental results demonstrate that the proposed system performed well in terms of minimal flickering and stable operation under both dimming modes. In the experimental procedure, the maximum LED current was set to 400 mA via RSET adjustment to analyze the effects of internal offset voltages and filtering circuits on current accuracy. The proposed implementation provides a compact and versatile structure suitable for applications in automotive interior lighting, household appliances, and space-constrained UV LED modules, and our results confirm that our approach is practical and feasible.

A robust internal reference voltage generation circuit with self-adaptive calibration scheme for receiver in NV-DDR interface

Grid-Forming Control of DFIG-Based Wind Turbine Generator by Using Internal Voltage Vectors for Asymmetrical Fault Ride-Through

The oscillations induced by voltage source converters (VSCs) in DC voltage timescale dynamics pose significant challenges to the safe and stable operation of VSC-dominated power systems. However, previous studies have conducted simplified analyses without fully understanding the fundamental roles of different timescale control loops in converter-interfaced systems. In light of this, this study first identifies the key state variables and operating points that directly characterize the energy storage levels of devices and networks in AC systems. A model for the converter-interfaced system is then established in the specified DC voltage timescale. The key contribution of this work is the proposal of an analytical framework that decomposes system stability into self-stabilizing (Self-stable) and externally coupled stabilizing (En-stable) paths based on internal voltage amplitude and frequency, aiming to reveal the physical mechanisms behind internal voltage amplitude and frequency oscillations in DC voltage timescale dynamics. Based on this framework, the Self-stable path and En-stable path of the internal voltage amplitude/frequency of converter-interfaced systems are derived. This novel analytical method mathematically decouples the stability of a single variable into a direct self-influence path and an indirect path coupled through other system variables. Subsequently, the causes of internal voltage amplitude/frequency oscillations in the specified voltage timescale are explained using the Self-stability and En-stability analysis method. A key finding of this study is that the stability of the internal voltage amplitude and frequency exhibits a dual relationship: for amplitude stability, the Self-stable path is stabilizing, whereas the coupled path is destabilizing; for frequency stability, the roles are reversed. Finally, the results are verified through simulations.

Abstract This paper introduces an analog spiking neuron that utilizes time-domain information, i.e. a time interval of two signal transitions and a pulse width, to construct a spiking neural network (SNN) for a hardware-friendly physical reservoir computing (RC) on a complementary metal-oxide-semiconductor platform. A neuron with leaky integrate-and-fire is realized by employing two voltage-controlled oscillators with opposite sensitivities to the internal control voltage, and the neuron connection structure is restricted by the use of only 4 neighboring neurons on a 2-dimensional plane to feasibly construct a regular network topology. Such a system enables us to compose an SNN with a counter-based readout circuit, which simplifies the hardware implementation of the SNN. Moreover, another technical advantage thanks to the bottom–up integration is the capability of dynamically capturing every neuron state in the network, which can significantly contribute to finding guidelines on how to enhance the performance for various computational tasks in temporal information processing. Diverse nonlinear physical dynamics needed for RC can be realized by collective behavior through dynamic interaction between neurons, like coupled oscillators, despite the simple network structure. With behavioral system-level simulations, we demonstrate physical RC through short-term memory and exclusive OR tasks, and the spoken digit recognition task with an accuracy of 97.7% as well. Our system is considerably feasible for practical applications and can also be a useful platform for studying the mechanism of physical RC.

The assessment of the parameters of the power section of the converter as part of an autonomous PV plant installation with a rated power of 5000 W, designed to supply consumers with single-phase sinusoidal voltage of 220 V 50 Hz using a transformerless circuit is provided. The structure of the proposed PV plant differs from traditional structures. In the proposed structure, the DC voltage converter is connected with its input to the series connection of solar panels and performs the function of maximizing the output power and its output is connected to the DC link – the battery. The voltage-source inverter with sin pulse-width modulation is also connected to this link, its output voltage is the supply voltage of consumers. The minimum battery voltage is slightly higher than the output voltage amplitude of the DC/AC inverter. In this structure, unlike common systems with a 48V battery, there is no need for a separate bidirectional DC/DC converter with the functions of ensuring the coordination of the battery voltages and the internal DC voltage link (about 400 V, from which the voltage-source inverter is powered) and ensuring the charge – discharge of the battery with a set power from half the nominal power and above. The absence of a separate DC/DC converter simplifies the system and allows to significantly increase its efficiency in the battery load power supply mode. In the article evaluates the parameters of the PV plant of the proposed structure: the required number of serial solar panels with a nominal power of 500 W is determined, the use of a step-down DC-DC converter circuit is justified, its PWM frequency (25 kHz) is selected and the capacitance of the input storage capacitor is estimated. For the switches of the DC-DC converter, it is proposed to use modern field-effect transistors with an insulated gate and Schottky diodes based on silicon carbide (SiC); the types of semiconductor devices are selected and the power loss estimate in them (about 12 W) is provided. The required number of series-connected accumulator battery cells is determined. For the DC/AC inverter using the bridge circuit, the MOSFET type and the PWM frequency of 25 kHz are selected. The use of a three-level (unipolar) PWM algorithm is proposed, which allows doubling the output current ripple frequency. Estimates of power losses in the DC/AC inverter switches are provided: about 50 W. It is proposed to use a sendust ring core for the output filter choke, the choke parameters are determined, including an estimate of power losses: about 7 W. An estimate of the efficiency of an autonomous PV plant is provided: in the solar panel power mode 98.3% versus 97.6% for the analog and 98.8% when powered by an accumulator battery versus 94% for the analog, which corresponds to a reduction in power losses of 1.4 – 5 times.

The impedance spectroscopic (IS) and electrical equivalent circuit analysis of the popular combination indium tin oxide/n‐CdS/p‐Si heterojunction solar cell is being carried out for the very first time in this analytical study using solar cell capacitance simulator (SCAPS)‐1d and the IS module. In this analysis, it is aimed to uncover the device's inherent characteristics for the purpose of future advancements and improvements. Various characteristics and features, as well as the basis for future device improvements, are uncovered through rigorous analysis of the following variables: forward/reverse bias voltage, frequency, illumination intensities (dark and light mode), wavelength‐dependent capacitance–voltage, conductance‐voltage, capacitance–frequency, and capacitance–temperature. Additionally, spectral response (SR) charts that rely on wavelength are created in addition to Nyquist and Bode plots. The acquired impedance data is much enhanced by these displays. Through IS analysis, a potent method for investigating the dynamics of electrical and ionic charges inside cells is revealed. An improved comprehension of the internal voltage and doping profile in the layers is achieved through additional Mott–Schottky analysis. Bode graphs that rely on frequency provide a better way to comprehend the device's dielectric characteristics. The SR curve can be better understood with the help of these graphs.

The emergence of threshold-based digital comparators has revolutionized mixed-signal circuit systems, notably in high-speed ADCs. These comparators generate internal reference voltage autonomously, eliminating reliance on external sources. However, different voltages require varied internal logic gate sizes, impacting reference voltage accuracy, power, latency, and area. This divergence prevents direct application of conventional digital synthesis to these comparators. In this study, EVSR (Extended Variable Self-built References) is proposed for automatic sizing optimization. A non-linear programming model is introduced to minimize internal voltage error, solved by an efficient single comparator sizing (SCS) algorithm based on integer differential evolution and Nelder-Mead mechanisms. Additionally, for multi-bit flash ADCs, the comparator is refined for a more uniform distribution of internal voltage. The optimization of both error and energy-delay product through optimal SCS and dynamic programming (OPTSCS-DP) is accomplished by the multi-comparator sizing algorithm. Experimental results confirm the SCS-based digital comparator reaches a step threshold of 10mV. Compared to the best existing solution at the same 55nm process, the proposed design reduces power consumption by 72.25% and area by 41.18%. And our proposed OPTSCS-DP demonstrates a enhancement in the Figure of Merit (FoM) compared to iterative SCS. (Code is available at https://github.com/ucas-xsw/DigitalCompapratorAlgorithm.)

An important applied problem is the selective extraction of valuable components from scrap alloys of refractory metals with iron group metals containing a significant amount of rare metals. The urgency and relevance of research in this area is due to the strategic importance of these metals for the defense industry of Ukraine, since alloys based on them have unique physical and mechanical properties and are used, in particular, as structural materials in gas turbine equipment and elements of military equipment. Given that the service life of aircraft turbojet engines depends on the resource of the most important components, increasing their reliability is possible through the use of high-purity components of heat-resistant nickel-based alloys. The hydrometallurgical method of processing spent parts from such alloys is energy-saving. Nickel transferred to the solution can be isolated chemically or electrochemically. Electroextraction is the most selective method of isolating high-purity nickel from the leaching solution. To extract nickel with low internal voltages, sulfur-containing organic substances are used, which are added to the electroextraction solution. In the presented work, a new type of leaching solution based on methanesulfonic acid, the salts of which are highly soluble in an aqueous medium, was investigated. Sodium orthoarylsulfonate and saccharinate were used as sulfur-containing internal voltage reducers. It was found that the separation of nickel from the methanesulfonate solution occurs at a lower polarization compared to the sulfate solution. The effect of sodium orthoarylsulfonate in an amount of 15 mmol/l on the kinetics of nickel separation from the methanesulfonate solution is insignificant. Sodium saccharinate increases the polarization of nickel separation from the methanesulfonate solution by 100 mV already at a concentration of 0.25 mmol/l. Nickel without internal stresses was obtained from methanesulfonate solution in the presence of 15 mmol/L orthoaryl sulfonate or 0.05 mmol/L sodium saccharinate. The range of current densities for obtaining stress-free nickel in the case of sodium orthoaryl sulfonate is 2–7 A/dm2, for sodium saccharinate this range is 2–5 A/dm2

This paper presents a new method for fault location in transmission lines with series compensation, using data from voltage and current measurements at both terminals, applied to artificial neural networks. To determine the fault location, we present the proposal of using current phasors, obtained from the oscillography recorded during the short circuit, as the input to the neural network for training. However, the method does not rely on the internal voltage values of the sources or their respective equivalent Thevenin impedances to generate training files for the neural network in a transient simulator. The source data are not known exactly at the time of the short circuit in the transmission line, leading to greater errors when neural networks are applied to real electrical systems of utility companies, which reduces the dependency on electrical network parameters. To present the new method, a conventional fault location algorithm based on neural networks is initially described, highlighting how the dependency on source parameters can hinder the application of the artificial neural network in real cases encountered in utility electrical systems. Subsequently, the new algorithm is described and applied to simulated and real fault cases. Low errors are obtained in both situations, demonstrating its effectiveness and practical applicability. It is noted that the neural networks used for real cases are trained using simulated faults but without any data from the terminal sources. Although we expect the findings of this paper to have relevance in transmission lines with series compensation, the new method can also be applied to conventional transmission lines, i.e., without series compensation, as evidenced by the results presented.

ABSTRACT The system frequency response (SFR) of doubly‐fed induction generator (DFIG) is different from synchronous generator (SG). A grid‐forming DFIG via virtual synchronous control of inner voltage vector is proposed to mimic the characteristics of SFR of SG, which leads to an internal voltage SFR model with virtual incremental mechanical power model similar to SG. For clearly demonstrating the inertial dynamics of grid‐forming DFIG, the expression of the inertial dynamics in the time domain is firstly derived by convolution, which has same inertial dynamics with SG at the beginning of disturbance. Moreover, the inertia difference between DFIG via synthetic inertia control and grid‐forming DFIG is analysed, and the fast power response capability of grid‐forming DFIG is demonstrated by comparison with synthetic inertia control. Furthermore, a multi‐machine SFR model is established by the proposed SFR model to analyse the impacts of different control parameters on the system frequency dynamics, which is convenient to coordinate with load disturbance. Finally, numerical simulations are conducted to show the effectiveness and accurateness of the proposed SFR model.

Modeling VSC with Active/Reactive Current Excitation and Internal Voltage Response for Grid Amplitude/Frequency Modulation Dynamics Analysis

The spiral generator, based on the principle of the electric field vector inversion, is capable of delivering repetitive high-voltage nanosecond pulses in the commercial portable pulsed x-ray source and gas switch trigger source. However, the spiral generator suffers from extremely low output efficiency, which significantly affects the compactness and accelerates the insulation film breakdown at electrode foil edges since the high charging voltage is required. A novel output efficiency improvement method for the spiral generator was proposed, implementing the permalloy film inside the passive layer to optimize internal voltage wave propagation processes during the pulser erection. Output characteristics and influential factors of the modified spiral generator are experimentally determined, and the wave propagation processes are analyzed. The significant output efficiency improvement (approximately from 10% to 30% combined with ferrite cores at the center) is seminal for the portable x-ray source and gas switch trigger source of compactness and long operation lifetime.

Grid-forming wind turbines (GFM-WTs) based on virtual synchronous control can support the voltage and frequency of power system by emulating the synchronous generator. The dynamic characteristics of a GFM-WT decided by virtual synchronous control, dq-axis voltage, and current control is significant for small-signal stability analysis. This paper builds a small-signal model of a GFM-WT in active power control (APC) and DC voltage control (DVC) timescale from the perspective of internal voltage. The proposed model describes how the magnitude and phase of the internal voltage are excited by the unbalanced active and reactive power when small disturbances occur. Interactions in different control loops can be identified by the reduced order model. We verify the accuracy of the proposed model in APC and DVC timescales by time domain simulations based on MATLAB/Simulink. Case studies show how the control parameters interact with each other in the two timescales.