Search Results (6,557)

With the large-scale integration of single-phase distributed photovoltaic systems into distribution grids, issues such as mismatched generation and load, overvoltage, and three-phase imbalance may arise in the distribution network. A multi-objective optimization method for flexible distribution networks incorporating a four-leg soft open point (F-SOP) is proposed based on fuzzy chance constraints. First, a mathematical model for the F-SOP’s loss characteristics and power control was established based on the three-phase four-arm topology. Considering the impact of source load uncertainty on voltage regulation, a multi-objective complementary voltage regulation architecture is proposed based on fuzzy chance constraint programming. This architecture integrates F-SOP with conventional reactive power compensation devices. Next, a multi-objective collaborative optimization model for distribution networks is constructed, with network losses, overall voltage deviation, and three-phase imbalance as objective functions. The proposed model is linearized using second-order cone programming. Finally, using an improved IEEE 33-node distribution network as a case study, the effectiveness of the proposed method was analyzed and validated. The results indicate that this method can reduce network losses by 30.17%, decrease voltage deviation by 46.32%, and lower three-phase imbalance by 57.86%. This method holds significant importance for the sustainable development of distribution networks. Full article

Conventional finite control set model predictive control (FCS-MPC) for permanent magnet synchronous motor (PMSM) drives suffers from a fundamental trade-off: shortening the control period improves current tracking but increases switching frequency and losses. This paper proposes a hysteresis-based variable control period MPC (HBVCP-MPC) to break this compromise. Unlike methods like direct torque control (DTC) and model predictive direct torque control (MPDTC) that use hysteresis to select voltage vectors (VV), our approach first selects the optimal VV via a cost function that balances current tracking accuracy and switching frequency. Hysteresis on the $dq$-axis currents is then employed solely to dynamically determine the application time of this pre-selected VV, which defines the variable control period. This grants continuous adjustment over the VV duration, enabling superior current tracking without a proportional rise in switching frequency. Experimental results confirm that the proposed method achieves enhanced steady-state performance at a comparable switching frequency. Full article

The Modular Multilevel Matrix Converter (M3C) has the potential to contribute to onshore grid frequency response by utilizing the electrostatic energy stored in its submodules. However, in the current offshore wind power domain, control schemes for M3C-based Low-Frequency AC transmission systems (M3C-LFACs) fail to effectively exploit the capacitor energy of M3C to provide adequate inertia support. Existing M3C controls are typically grid-following and thus suffer from stability issues under weak-grid conditions. To address this challenge, a dual-port grid-forming control strategy for M3C-LFAC systems is proposed, based on an energy synchronization loop. This approach enables phase-locked-loop-free synchronization between the M3C and the grid while establishing low-frequency link voltage vectors. Building on this foundation, an optimized energy utilization method for M3C total energy is introduced, featuring a two-stage preset curve to maximize the system’s inherent energy for frequency response. Under varying levels of grid load disturbances, the proposed scheme ensures that M3C-LFAC systems can provide optimal inertia support. Finally, simulation studies in MATLAB 2024b/Simulink validate the effectiveness and advantages of the proposed method. Full article

In this study, structural optimization and trap effect analysis of a 4H-SiC–based p–i–n betavoltaic (BV) cell were performed using Silvaco ATLAS TCAD (version 5.30.0.R) simulations combined with an electron-beam (e-beam) irradiation model. First, the optimum device structure was derived by varying the thickness of the intrinsic layer (i-layer), the thickness of the p-layer, and the doping concentration of the i-layer. Under 17 keV e-beam irradiation, the electron–hole pairs generated in the i-layer were effectively separated and transported by the internal electric field, thereby contributing to the short-circuit current density (J_SC), open-circuit voltage (V_OC), and maximum output power density (P_{out_max}). Subsequently, to investigate the effects of traps, donor- and acceptor-like traps were introduced either individually or simultaneously, and their densities were varied to evaluate the changes in device performance. The simulation results revealed that traps degraded the performance through charge capture and recombination, with acceptor-like traps exhibiting the most pronounced impact. In particular, acceptor-like traps in the i-layer significantly reduced V_OC from 2.47 V to 2.07 V and P_{out_max} from 3.08 μW/cm² to 2.28 μW/cm², demonstrating that the i-layer is the most sensitive region to performance degradation. These findings indicate that effective control of trap states within the i-layer is a critical factor for realizing high-efficiency and high-reliability SiC-based betavoltaic cells. Full article

Fe₄₂Mn₂₈Cr₁₅Co₁₀Si₅ is a highly strain-hardenable high-entropy alloy (HEA) that is challenging to machine with traditional metal cutting tools. The electrochemical behavior of this HEA was examined in nitrate- and chloride-based electrolytes to understand the electrochemical machining (ECM) process. Potentiodynamic and potentiostatic tests were conducted on this alloy in 1 M and 2.35 M NaNO₃ solutions, with and without additions of 0.01 M nitric acid and 0.01 M citric acid. A 20% NaCl solution was also tested as an electrolyte. Nitrate solutions caused passivation of the HEA, while no passivation was observed in chloride solutions. Surface analysis with X-ray photoelectron spectrometry (XPS) indicated that adding citric acid helped reduce surface passivation. The Faradaic efficiency of ECM increased with higher applied voltage. The chloride solution showed higher Faradaic efficiency than nitrate-based solutions. Specifically, the Faradaic efficiency of 20% NaCl at 10 V is 57.4%, compared to 21.9% for 20% NaNO₃ + 0.01 M citric acid at 10 V. Electrochemical parameters, including anodic and cathodic exchange current densities, Tafel slopes, and corrosion current densities, were calculated from the experimental data. The corrosion current densities in the 20% nitrate solutions ranged from 2.35 to 3.2 × 10⁻⁵ A/cm², while the 20% chloride solution had a lower corrosion rate at 1.45 × 10⁻⁵ A/cm². These electrochemical parameters can help predict the dissolution behavior of the HEA in nitrate and chloride solutions and aid in optimizing the ECM process. Full article

The thermal behavior of 3D-printed polyvinylidene fluoride (PVDF)-based composites enhanced with carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and their hybrid formulations was investigated under Joule heating at applied voltages of 2, 3, and 4 V. The influence of filler type and weight fraction on both electrical and thermal conductivity was systematically assessed using a Design of Experiments (DoE) approach. Response Surface Methodology (RSM) was employed to derive an analytical relationship linking conductivity values to filler loading, revealing clear trends and interaction effects. Among all tested formulations, the composite containing 6 wt% of GNPs exhibited the highest performance in terms of thermal response and electrical conductivity, reaching a steady-state temperature of 88.1 °C under an applied voltage of just 4 V. This optimal formulation was further analyzed through multiphysics simulations, validated against experimental data and theoretical predictions, to evaluate its effectiveness for potential practical applications—particularly in de-icing systems leveraging Joule heating. The integrated experimental–theoretical–numerical workflow proposed herein offers a robust strategy for guiding the development and optimization of next-generation polymer nanocomposites for thermal management technologies. Full article

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This study develops a data–model hybrid-driven simulation architecture that integrates a Federated Evolutionary Monte Carlo Optimization (FEMCO) algorithm for distribution network optimization. The model-driven module employs spectral clustering to decompose the network into multiple autonomous subsystems and performs distributed reconstruction through gradient descent. The data-driven module, built upon Long Short-Term Memory (LSTM) networks, learns temporal dependencies between load curves and operational parameters to enhance predictive accuracy. These two modules are fused via a Random Forest ensemble, while FEMCO jointly leverages Monte Carlo global sampling, Federated Learning-based distributed training, and Genetic Algorithm-driven evolutionary optimization. Simulation studies on the IEEE 33 bus distribution system demonstrate that the proposed framework reduces power losses by 25–45% and voltage deviations by 75–85% compared with conventional Genetic Algorithm and Monte Carlo approaches. The results confirm that the proposed hybrid architecture effectively improves convergence stability, optimization precision, and adaptability, providing a scalable solution for the intelligent operation and distributed control of modern power distribution systems. Full article

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and perform valid statistical inference on the coefficients. Next, it trains a performance-optimized LR classifier with class-balanced sample weighting to produce calibrated violation probabilities. LR maps VMI to violation probability and analytically converts a calibrated probability threshold into an operator-ready VMI decision boundary. Applying 5-fold group cross-validation to 8816 node-level samples generated from a 22.9 kV Jeju Island model yields performance- and safety-oriented probability thresholds (θ_opt = 0.7891, θ_safe = 0.6880), which correspond to VMI decision boundaries VMI_DB,opt = 0.7893 and VMI_DB,safe = 0.8101. On an unseen 20% test set, the LR classifier achieves 99.94% accuracy (F₁ = 0.9977) under θ_opt and 100% recall under θ_safe. A random forest (RF) benchmark confirms comparable accuracy (=99.72%) but lacks analytical invertibility and transparency. This framework offers distribution system operators (DSOs) and virtual power plant (VPP) operators clear, evidence-based criteria for routine planning and risk-averse decision-making, and it can be applied directly to any distribution system with node-level voltage measurements and known regulation limits. Full article

To address the issue of frequent power frequency overvoltage disconnection accidents in offshore WF caused by the capacitive effect of submarine cables, this paper proposes a double-layer RP optimal configuration method for large-scale offshore WF based on an adaptively improved GSA. Firstly, this paper considers both the RP capabilities of offshore WT themselves and RP compensation equipment, designing a two-layer “configuration-control” optimization framework for RP. The upper layer establishes an optimization configuration model with the objective of minimizing the total investment cost and operational expenses of the equipment. The lower layer establishes a RP optimization operation model with the objective of minimizing a weighted index that comprehensively considers system network losses, voltage deviations, and RP capacity margins. Then, to address the issue of traditional GSA being prone to local optima, this paper introduces a random factor into the mass calculation, combines elite concepts to selectively synthesize gravitational forces based on fitness values, and assigns larger random numbers to forces corresponding to superior particles. By introducing control parameters to adaptively update particle positions, an adaptively improved GSA is proposed, which is employed to solve the established double-layer RP optimization configuration model for large-scale offshore WF. Finally, simulation analysis is conducted on a large-scale offshore WF constructed using MATLAB R2020a. Compared with the basic GSA algorithm, the proposed method reduces the system loss by 50.59% and the voltage deviation by 64.75%. The research demonstrates that the proposed method can effectively enhance the stability of grid voltage and proves the effectiveness of the improved GSA and the proposed two-layer “configuration-control” optimization model. Full article

Reliable multilevel inverter IGBT modules require precise loss and heat management, particularly in severe traction applications. This paper presents a comprehensive modeling framework for three-level T-type neutral-point clamped (TNPC) inverters using a high-power Insulated Gate Bipolar Transistor (IGBT) module that combines model predictive control (MPC) with space vector pulse width modulation (SVPWM). The particle swarm optimization (PSO) algorithm is used to methodically tune the MPC cost function weights for minimization, while achieving a balance between output current tracking, stabilization of the neutral-point voltage, and, consequently, a uniform distribution of thermal stress. The proposed SVPWM-MPC algorithm selects optimal switching states, which are then utilized in a chip-level loss model coupled with a Cauer RC thermal network to predict transient chip-level junction temperatures dynamically. The proposed framework is executed in MATLAB R2024b and validated with experiments, and the SemiSel industrial thermal simulation tool, demonstrating both control effectiveness and accuracy of the electro-thermal model. The results demonstrate that the proposed control method can sustain neutral-point voltage imbalance of less than 0.45% when operating at 25% load and approximately 1% under full load working conditions, while accomplishing a uniform junction temperature profile in all inverter legs across different working conditions. Moreover, the results indicate that the proposed control and modeling structure is an effective and common-sense way to perform coordinated electrical and thermal management, effectively allowing for predesign and reliability testing of high-power TNPC inverters. Full article

Lithium-ion batteries are extensively used for energy storage in renewable, electronic, and automotive applications. However, once their electrical capacity is exhausted, they become hazardous waste that requires energy-intensive recycling processes. This study investigates the thermodynamic and exergetic behavior of LiMn₂O₄-based lithium-ion batteries subjected to controlled electrical overvoltage from renewable energy sources, aiming to quantify their potential for thermal energy generation and recovery. A detailed mathematical model was developed to describe the coupled heat transfer and electrochemical phenomena occurring during overvoltage conditions, and experimental validation was performed under various voltage levels and charging states. Energy and exergy analyses were applied to determine the configuration yielding the highest conversion efficiency for both new and aged cells. The maximum thermal energy efficiency reached 81% for new batteries and 4% for used batteries, while the corresponding exergetic efficiencies were 5% and 1.6%, respectively. Although this study does not propose the immediate large-scale reuse of spent batteries as thermal devices, the results provide quantitative insight into irreversible energy conversion processes and highlight their potential contribution to waste heat recovery and energy optimization strategies in sustainable industrial systems. This thermodynamic framework offers a novel approach for valorizing end-of-life batteries within circular energy models, reducing environmental impact, and advancing the integration of renewable energy-driven heat recovery technologies. Full article

The growing demand for improved interruption performance characteristics in emerging aircraft high-voltage direct current (HVDC) electrical networks motivates the rapid development of solid-state power controllers (SSPCs). This article presents a comprehensive design procedure for a 270 V 300 A SSPC utilizing discrete SiC cascode devices. Due to the high fault current and limited power of single switches, parallel SiC FETs are essential for interrupting high fault currents in SSPCs. Consequently, the challenge of current balancing among parallel devices is addressed in this paper by adopting a passive current balancing strategy based on an irregular-shaped busbar. Furthermore, considering the voltage spikes arising from the power loop parasitic inductance and TVS characteristics during fault interruption, an RC-TVS-based transient voltage mitigation circuit is proposed to suppress these peak voltages. Moreover, thermal models for overload and short-circuit conditions were developed to optimize the thermal management system to ensure reliable operation of the SSPC. Finally, a prototype of 270 V/300 A SSPC has been built to validate the key characteristics of the proposed high power SSPC. Full article

High-voltage fuses are critical safety components in electric vehicle (EV) battery systems, yet their thermal behavior under charging currents remains insufficiently characterized. This study develops and validates a physics-based thermal resistor-capacitor (RC) network model of a high-voltage melting fuse, accounting for copper elements, quartz sand filling, and polyester casing. Experimental accelerated life tests and current step load profiles were performed in a climate chamber at 70 °C, with temperature measurements at the fuse terminals. The RC model was constructed using material properties and geometry-derived parameters, including three copper element sections, one quartz sand node, and one case node. A discretized state–space formulation was implemented to simulate the transient thermal behavior. The results reveal distinct dynamic and stationary characteristics, with thermal time constants varying strongly between fuse sections. Comparisons with experimental data demonstrate that the proposed model captures both rise time and steady-state behavior, with deviations attributable to contact resistances and parasitic effects. The findings highlight that charging currents in practical profiles typically remain below 50% of fuse current ratings, leaving optimization potential for higher permissible currents, faster charging, and reduced downtime while maintaining safety. The outcome of this model is highly relevant for lifetime prediction models. Full article

With continued CMOS scaling, transistor miniaturization has significantly raised SRAM integration density while lowering the critical charge (Qc), increasing cell vulnerability to spaceborne high-energy particles. Single-event upset (SEU) and especially single-event multiple node upsets (SEMNU) due to charge sharing present major reliability challenges. To overcome these issues, this study introduces a radiation-hardened 20T SRAM cell with read/write optimization (RWO-20T) designed for space applications. Benchmarking against hardened cells RH14T, RHSCC16T, S8P8N16T, and CC18T reveals that RWO-20T delivers superior read static noise margin (RSNM), increased word-line write trip voltage (WWTV), and faster read and write access times. Although the higher transistor count incurs some area overhead and slightly lowers the hold static noise margin (HSNM), RWO-20T achieves improved recovery rates for dual-node upsets (DNU) and triple-node upsets (TNU) under SEMNU conditions. The circuits were simulated in a 90 nm CMOS process and operated at 1 V. Full article

In the post-Moore era, carbon nanotube field-effect transistors (CNTFETs) are a promising alternative to complementary metal-oxide-semiconductor (CMOS) technology at and below the 5 nm node. Compact models bridge circuit design and device physics, yet the electrostatic discharge (ESD) behavior of CNTFETs remains insufficiently captured. Focusing on the local bottom-gate (LBG) CNTFET structure, which offers enhanced gate control due to its bottom-gate configuration, this paper investigates three dominant ESD-triggering mechanisms—thermionic current, tunneling leakage current, and thermal failure breakdown. Then, a hybrid compact–behavioral ESD model for CNTFETs is established. After theoretical derivation and comparison with test results, the model parameters are optimized through fitting. The simulation results exhibit excellent agreement with CNTFET measurements, particularly capturing the Human Body Model (HBM) pre-charge threshold phenomenon at 72 V and accurately predicting the subsequent voltage collapse behavior. This validates the accuracy and effectiveness of the model, laying a theoretical and experimental foundation for further construction of carbon-based standard-cell and I/O libraries. Full article