Search Results (3,772)

The widespread integration of wind energy conversion systems has fundamentally reshaped modern power grid architecture. However, the limited dynamic response of wind turbine (WT) converters during grid faults—particularly their inability to provide sufficient reactive current and maintain voltage stability under severe dips—necessitates a redefinition of the conventional low-voltage ride-through (LVRT) curve. This study addresses this challenge by proposing a Mutation-Adaptive Mean Variance Mapping Optimization (A-MVMO) algorithm for the control of grid-side converters (GSCs) in wind farms (WFs). To systematically assess post-fault voltage recovery, a Time-Segmented Analysis for Voltage Recovery (T-SAVR) approach is developed with a multi-objective function. The performance of the proposed A-MVMO is benchmarked against standard MVMO and conventional particle swarm optimization (PSO) under both moderate (0.7 pu) and severe (0.15 pu) voltage dips using the IEEE 39-bus system implemented in DIgSILENT/PowerFactory. The results demonstrate that A-MVMO achieves fast, oscillation-free voltage recovery with negligible overshoot (<1%) and lower current injection than PSO and MVMO, while satisfying all engineering constraints. Moreover, the co-optimization of Park-level and turbine-level controllers ensures seamless coordination, as evidenced by the close tracking between the farm-wide reactive power reference and the aggregated turbine response. The T-SAVR method proves essential for focusing optimization on controllable recovery dynamics, yielding a superior LVRT curve. Full article

With the increasing penetration of wind and solar renewable energy into modern power systems, grids exhibit ‘dual-high’ (i.e., a high proportion of both renewable energy and power electronic devices) and ‘dual-low’ (i.e., low equivalent rotational inertia and low short-circuit capacity) structural characteristics. This leads to critical challenges, notably insufficient short-circuit capacity, declining voltage and frequency stability, and weakened system damping. To address the stability requirements of new power systems, this study proposes and systematically investigates a variable-speed synchronous condenser based on AC excitation technology. The research encompasses the operational principles, starting mechanisms, and control strategies of the device, with a particular focus on analyzing its stator-flux-oriented vector control method and active–reactive power decoupling regulation mechanism. By independently adjusting the frequency, amplitude, and phase of the AC excitation on the rotor side, the system achieves a millisecond-level dynamic reactive power response, rapid frequency support, and self-starting capability without the need for external starting devices. To validate the effectiveness of the theoretical analysis and engineering practicality, this study presents grid-connected operational tests using a 3600 kVar engineering prototype at a wind farm. The test results demonstrate that the variable-speed synchronous condenser performs excellently in speed regulation, dynamic reactive power response, and primary frequency modulation. It effectively provides short-circuit capacity, enhances system damping, and significantly improves the voltage and frequency stability of power grids with high penetration of renewable energy. This study offers innovative technical pathways and empirical evidence for constructing a stability support system that meets the developmental needs of new power systems. It holds significant theoretical value and engineering guidance for promoting the smooth transition of power grids from synchronous machine-dominated to power electronics-based architectures. Full article

Epoxy-glass-mica composite materials are widely used as electrical insulating materials in high-voltage rotating machinery due to their layered structure and excellent dielectric properties. Taking the F-class epoxy glass with a small amount of rubber powder mica tape commonly used as the main insulation of wind turbine stator coils as the research object, 7-day, 14-day, 21-day, and 28-day low-temperature treatment tests were conducted at −50 °C. The surface morphology and chemical structure changes of the materials were characterized by SEM and FTIR, and the influence laws of low-temperature treatment on the electrical properties of the mica tape insulation materials were systematically studied. The experimental results show that the low-temperature environment will induce microcracks and interface delamination and other structural damages, but no obvious change in the chemical structure of the mica tape was observed. With the extension of the low-temperature treatment time, the electrical properties of the mica tape show a deteriorating trend, and after 28 days of low-temperature treatment, the breakdown field strength of the F-class mica tape decreased by approximately 18.5%, and the volume conductivity overall increased by about two orders of magnitude. This indicates that the microcrack defects induced by low-temperature will lead to an enhanced electrical-thermal coupling effect in the insulation structure, thereby accelerating the degradation process of the insulation material. This reveals the degradation mechanism of wind turbine stator main insulation from “structural damage” to “performance degradation” and then to “insulation aging” under low-temperature conditions, providing a theoretical basis for the design and reliability assessment of insulation systems in wind turbine generators in cold regions. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article

Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines. Full article

This article presents the design and characterization process of a lightweight Vivaldi antenna for high-voltage ultra-wideband systems. The proposed antenna consists of two radiating arms with different exponential curves on their inner and outer edges fed with an insulated-coplanar-plates transmission line. Weight reduction is achieved by implementing the antenna with sheets composed of a polyester layer between two aluminum layers, with a polylactic acid insulator inserted between the arms. The reflection coefficient of the implemented antenna demonstrates an impedance bandwidth ranging from 0.61 GHz to 3.44 GHz. High-voltage operation of up to 12.4 kV is also experimentally demonstrated. In addition to satisfying the high-voltage and ultra-wideband operational requirements, the proposed antenna is shown to achieve, among antennas with comparable characteristics, the most effective combination of low minimum operating frequency and low weight. The transfer function between the voltage applied to the antenna, $V_s$, and the radiated electric field, $E_r$, is measured. Using this transfer function, the radiated electric field is calculated for an input voltage pulse with a rise time of 110 ps to confirm the antenna’s capability of producing radiated pulses with low distortion. The calculated radiated electric field pulse closely matches the results obtained with full-wave simulation. To assess the similarity between the radiated and applied pulses, the pulse width stretch ratio is calculated, yielding a variation of 3.86% for the direction of maximum gain and 9.36% for 30° in the H-plane of the antenna. This feature is desirable for EMC, EMI and sensing applications. The antenna is also characterized in the frequency domain, achieving a maximum gain of 10.09 dBi at 3.63 GHz and a 30° 3 dB beamwidth for ultra-wideband pulses. Full article

Background: Electrochemotherapy enhances the intracellular delivery of anticancer agents through electroporation but is traditionally limited to cytotoxic drugs associated with significant side effects. Vitamin C (ascorbic acid) exhibits selective anticancer activity when accumulated at high intracellular concentrations; however, its therapeutic application is restricted by poor membrane permeability and rapid systemic clearance. Methods: In this study, we investigated whether reversible electroporation, applied using a custom-designed variable plate electrode system designed to deliver a uniform electric field, could potentiate the antitumor efficacy of low-dose vitamin C. Numerical simulations were performed to optimize electrode spacing and stimulation voltage, suggesting homogeneous electric field coverage throughout the tumor volume. The proposed approach was evaluated in vitro using MDA-MB-231 and 4T1 breast cancer cell lines and in vivo in a 4T1 murine breast cancer model. Results: Low-dose vitamin C alone produced minimal cytotoxic effects, whereas its combination with electroporation significantly reduced cell viability and increased apoptotic and necrotic cell death in vitro. In vivo, vitamin C–assisted electrochemotherapy resulted in pronounced tumor growth suppression, with tumor volumes reduced to approximately 0.34-fold of baseline by day 15, accompanied by decreased proliferation and marked tissue disruption. Conclusions: These findings demonstrate that uniform-field reversible electroporation markedly enhances the intracellular delivery and antitumor activity of low-dose vitamin C, supporting this technology-driven strategy as a promising, low-toxicity alternative to conventional chemotherapeutic agents in electrochemotherapy for solid tumors. Full article

With the continuous increase in the number of Retired Lithium-Ion Batteries (RLBs), accurately estimating their Maximum Remaining Capacity (MRC) has become a key challenge for rapid sorting and secondary utilization. Conventional detection methods often suffer from low efficiency and limited scalability for large-scale applications. To address these issues, this paper presents a rapid MRC estimation method using a hybrid Convolutional Neural Network (CNN), Conv Block Attention Module (CBAM), and Long Short-Term Memory (LSTM) architecture. The proposed approach extracts key voltage and capacity features from only the initial 30 min charging phase, integrating both factory and laboratory data. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies, and the CBAM adaptively emphasizes critical characteristics. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, achieving a testing R² of 98.05% and a Mean Absolute Percentage Error (MAPE) of 1.60%. These results highlight the superior performance of the proposed framework, exhibiting strong potential for high-throughput battery sorting and large-scale health monitoring systems. Full article

Portable devices are powered in direct current (DC) or by batteries (primary battery), accumulators (secondary battery), and now supercapacitors, which can also be used for energy storage. The European Portable Battery Association states that approximately 239,000 tons of batteries were placed on the market in the European Economic Area (EEA) plus Switzerland in 2022. Even if they were all disposed of correctly respecting the 3R paradigm (Reduce, Reuse and Recycle), non-rechargeable batteries create an environmental problem because they do not discharge completely with an obvious waste of energy. Secondary batteries and supercapacitors can be recharged because they use reversible chemical/physical processes while primary batteries cannot be recharged because they are based on irreversible redox reactions; nevertheless, it is possible to try to recover their residual charge if this is higher than a threshold beyond which the reactions can be reversible. The most used batteries are alkaline zinc/manganese dioxide and they are non-rechargeable; an inappropriate recharge attempt can lead to serious harm to the operator and the environment. This paper describes a simple Arduino-based circuit and the protocol to measure and graph the residual charge of an alkaline battery in order to establish if it can be recharged. The circuit, design, the Arduino Uno R3 sketch (i.e., microprocessor software) and the full protocol are here presented under the open source license (Copyright Creative Commons Public license, CC BY-NC-ND 4.0 EN) so that they could become a pilot system and then a commercial product. The residual charge of 158 batteries, obtained after discharging those that, by eye, appeared damaged, was measured. Results evidenced that 49% of batteries had a residual voltage, under low load, between 1.2 and 1.6 V, making them good candidates for a recharge attempt. Full article

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

High-temperature proton exchange membrane fuel cells (HT-PEMFCs) coupled with methanol reforming hold promise for distributed energy systems, yet channel hydrodynamics and geometry optimization remain underexplored. This study develops a 3D multiphysics model to investigate coupled behaviors in HT-PEMFCs fueled by methanol reformate. Results reveal bifurcation-induced Dean vortices have dual effects: they cause flow maldistribution (15–18% velocity deviation) and contribute 50% of inlet pressure loss, while generating a lateral pumping effect that enhances local mass transfer. A continuous parametric sweep of channel widths (0.9–1.9 mm) identifies a voltage-dependent performance crossover—narrower channels (1.3 mm) excel at high voltages by improving electronic conduction, whereas wider channels (1.5 mm) perform better at low voltages by mitigating mass transfer limitations. These findings provide quantitative design criteria for optimizing flow field geometry in HT-PEMFC stacks. Full article

Transformers are key devices in the new electricity system, and the entire life cycle is associated with a considerable resource consumption and carbon footprint (CF). Understanding CF is essential for accelerating the low-carbon transition of the industry. Therefore, a systematic CF model for transformers is constructed in this study based on life cycle assessment (LCA). The results indicate that the operation stage is the overwhelmingly dominant phase for CF of transformer, with electricity acting as the main carbon source. The CF at the raw-material stage mainly originates from steel and copper. Through analysis, eight key impact factors were identified, leading to the formulation of three-dimensional carbon reduction pathways. It was observed that a 10% reduction in total losses of a transformer results in an approximate 10% decline in CF. At the same time, the transition of the electricity grid to clean energy helps reduce CF during operation. In addition, the effectiveness of a multi-factor carbon reduction pathway was examined. The results showed that, under this integrated pathway, the CF across all transformer rated voltages could be reduced by 9.75%. Based on this, a system pathway centered on enhancing operational energy efficiency is proposed, supported by green materials and processes, and coordinated through smart operation and maintenance, and circular recycling. This provides quantitative evidence and decision support for the green transition of transformers, contributing to the broader goals of sustainability development in electricity system. Full article

This paper presents a lightweight method for assessing the resilience of power distribution systems that integrates climate and infrastructure data through impact chains and a probabilistic approach, while minimizing data integration and implementation complexity. The method is demonstrated for heatwave hazards by combining network characteristics, failure probabilities of heat-sensitive components (e.g., medium-voltage cable joints), and location-specific climate projections to generate spatial maps of failure risk and network resilience. These maps support the identification and prioritization of critical components requiring intervention. Critical segments are then further analyzed using probabilistic resilience metrics to compare alternative adaptation strategies. Overall, this work contributes a practically applicable, low-complexity methodology for identifying the weakest portions of distribution networks, along with a more in-depth probabilistic approach for assessing their climate resilience. The com-prehensive framework is illustrated through a case study of a representative portion of the Italian electricity distribution system in the urban area of Rome. It is implemented in a test environment that reflects realistic distribution network data structures and automatically integrates climate data from established online repositories. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article

Background/Objectives: Electroanatomic mapping (EAM) provides high-resolution spatial and electrogram information, but the prognostic utility of quantitative EAM features has not been systematically evaluated with contemporary artificial intelligence (AI) methods. We investigated whether an AI analysis of quantitative EAM exports from the CARTO system enhances the prediction of major arrhythmic events (MAEs). Methods: In this retrospective, multicenter cohort study, 248 consecutive patients undergoing left ventricular EAM at four tertiary electrophysiology centers were analyzed. Numerical EAM descriptors (spatial coordinates, unipolar/bipolar voltages, local activation time, impedance) were transformed into derived metrics, including local activation heterogeneity (GR), late-potential extent (LAT), bipolar–unipolar discrepancy (VLT), and low-amplitude scar extent (Scar Areas), and were spatially normalized via spherical projection. Clinical, anamnestic, and imaging variables were integrated. Machine learning and deep learning models were trained with an 80:20 train/test split and evaluated using three-fold cross-validation. Performance metrics included area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision. Results: Models incorporating both clinical and AI-processed EAM features achieved high discriminatory performance (test AUC up to 0.92; accuracy up to 0.896). Specificity was consistently high (≈0.97–0.998), whereas sensitivity remained modest (≈0.39–0.58). Among the EAM-derived features, GR was the most consistently informative predictor across algorithms and analyses; VLT, LAT, and Scar Areas also contributed substantially. Regionally, basal sub-mitral, subaortic, and posterolateral basal-to-mid zones exhibited the strongest associations with MAEs. Conclusions: AI-driven quantitative analysis of left ventricular EAM exports augments risk stratification for MAEs beyond conventional clinical and binary EAM descriptors. Reflecting local conduction heterogeneity, GR emerged as the dominant EAM predictor. Prospective validation in larger, disease-specific cohorts and real-time integration within EAM platforms are warranted. Full article