Search Results (2,053)

With the rapid rise in electric vehicle (EV) adoption, the deployment of EV charging infrastructure—particularly fast-charging stations—has expanded significantly to meet growing energy demands. While fast charging offers the advantage of reduced charging time and improved user convenience, it imposes considerable stress on existing power distribution systems due to its high power and current requirements. This study investigated the impact of EV fast charging on power quality within Thailand’s distribution network, emphasizing compliance with accepted standards such as IEEE Std 519-2014. We developed a control-oriented EV-charging station model in power systems computer-aided design and electromagnetic transients, including DC (PSCAD/EMTDC), which integrates grid-side vector control with DC fast-charging (CC/CV) behavior. Active/reactive power setpoints were mapped onto $dq$ current references via Park’s transformation and regulated by proportional integral (PI) controllers with sinusoidal pulse-width modulation (SPWM) to command the voltage source converter (VSC) switches. The model enabled dynamic studies across battery state-of-charge and staggered charging schedules while monitoring voltage, current, and total harmonic distortion (THD) at both transformer sides, charger AC terminals, and DC adapters. Across all scenarios, the developed control achieved grid-current THDi of <5% and voltage THD of <1.5%, thereby meeting IEEE 519-2014 limits. These quantitative results show that the proposed, implementation-ready approach maintains acceptable power quality under diverse fast-charging patterns and provides actionable guidance for planning and scaling EV fast-charging infrastructure in Thailand’s urban networks. Full article

This paper proposes a design and control methodology for a Quasi-Y-Source impedance source inverter (QS-YSI) as a power electronics interface for Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) applications in the context of virtual power plants (VPPs). The work presents an analysis of bidirectional power transfer using Electric Vehicles (EVs) to supply power to the utility grid, businesses, and homes, thereby acting as distributed energy resources. The proposed QS-YSI topology supports both V2G and G2V operation while providing reactive power compensation and enabling the decoupled tracking of active power (P) and reactive power (Q), demonstrating the capability of EVs to return energy to the grid and to provide ancillary services such as power factor correction. The key contributions are a detailed control design methodology that includes pulsating DC-link voltage regulation, inverter output current reference tracking in the synchronous $dq$ reference frame considering DC-link voltage dynamics, and a modified Pulse Width Modulation (PWM) technique for effective decoupling of DC link and inverter output current control. Finally, the feasibility and validity of the proposed approach are demonstrated through simulations of the complete system under nominal conditions and experiments conducted considering a small-scale prototype. Full article

With the continuous increase in renewable energy penetration, traditional frequency regulation strategies in power grids struggle to maintain frequency stability under high renewable-share conditions. To address the shortcomings of the current deadband settings in regional grid frequency regulation, this paper proposes an optimized deadband-configuration scheme for renewable energy power plants and evaluates its effectiveness in enhancing the frequency regulation potential of renewable units. By developing frequency response models for thermal power, wind power, photovoltaic generation, and energy storage, the impact of different deadband widths on dynamic frequency response and steady-state deviation is analyzed. Three representative output scenarios for renewable units are constructed, and under each scenario the coordinated control performance of the proposed and the existing deadband configurations is compared. Simulation studies are then conducted based on a typical high renewable penetration scenario. The results show that, compared with the existing regional-grid deadband settings, the proposed configuration more fully exploits the regulation potential of renewable units, improves overall frequency-response capability, significantly reduces frequency deviations, and shortens recovery time. This research provides both theoretical foundations and practical guidance for frequency-support provision by renewable energy power plants under high penetration conditions. Full article

Urban trees play a crucial role in regulating hydrological processes within urban ecosystems by intercepting rainfall to effectively reduce surface runoff and mitigate urban flooding. Current research lacks a systematic quantification of rainfall interception capacity and its community-level impacts at the urban scale. This study adopts a city-scale perspective, integrating field survey data with the i-Tree Eco model to systematically explore the contributions of 20 factors to the average annual rainfall interception of tree species and the average annual rainfall interception efficiency of communities. The study revealed that Deciduous broadleaf trees (1.28 m³ year⁻¹) and Pure coniferous forests (90.7 mm year⁻¹) exhibited substantial rainfall interception capacity. Relative Height, Average Tree Height, Average Crown Width, and Planting Density of trees significantly influence interception capacity. Urban planning can optimize the selection of tree species (e.g., Paulownia, Populus tomentosa, etc.) and community structure (e.g., mixed planting of conifers and deciduous broadleaf trees) to improve rainfall interception capacity, thereby effectively reducing stormwater runoff, mitigating the risk of urban flooding. These findings provide a scientific basis for designing urban vegetation to mitigate flooding, support water management, and advance sponge city development. Full article

With the widespread application of multilevel inverters, device losses have become a critical area of research. A key limitation of conventional three-level discontinuous pulse width modulation (DPWM) strategies is their inability to maintain switching device clamping during the peak intervals of the load current, especially under varying load power factor conditions, thereby reducing switching losses. This paper proposes an improved three-level power factor adaptive DPWM (PFA-DPWM) strategy that minimizes switching losses by clamping the power devices during the one-third fundamental period of maximum load current. First, a unified mathematical model of DPWM strategies is established. Theoretical analysis demonstrates that phase disposition (PD) carrier modulation for three-level inverter exhibits superior line voltage harmonic characteristics. Based on this, a theoretical comparison of switching losses and harmonic distortion for various DPWM schemes is conducted. The proposed PFA-DPWM control strategy has the minimum switching loss without compromising harmonic performance. The efficacy and validity of the proposed strategy are confirmed by comprehensive simulation and experimental results. Full article

The precise identification, classification, sorting, and rapid and accurate quality assessment of soybean seeds are extremely important in terms of the continuity of agricultural production, varietal purity, seed processing, protein extraction, and food safety. Currently, commonly used methods for the identification and quality assessment of soybean seeds include morphological analysis, chemical analysis, protein electrophoresis, liquid chromatography, spectral analysis, and image analysis. The use of image analysis and artificial intelligence is the aim of the presented research, in which a method for the automatic classification of soybean varieties, the assessment of the degree of damage, and the identification of geometric features of soybean seeds based on numerical models obtained using a 3D scanner has been proposed. Unlike traditional two-dimensional images, which only represent height and width, 3D imaging adds a third dimension, allowing for a more realistic representation of the shape of the seeds. The research was conducted on soybean seeds with a moisture content of 13%, and the seeds were stored in a room with a temperature of 20–23 °C and air humidity of 60%. Individual soybean seeds were scanned to create 3D models, allowing for the measurement of their geometric parameters, assessment of texture, evaluation of damage, and identification of characteristic varietal features. The developed 3D-CNN network model comprised an architecture consisting of an input layer, three hidden layers, and one output layer with a single neuron. The aim of the conducted research is to design a new, three-dimensional 3D-CNN architecture, the main task of which is the classification of soybean seeds. For the purposes of network analysis and testing, 22 input criteria were defined, with a hierarchy of their importance. The training, testing, and validation database of the SB3D-NET network consisted of 3D models obtained as a result of scanning individual soybean seeds, 100 for each variety. The accuracy of the training process of the proposed SB3D-NET model for the qualitative classification of 3D models of soybean seeds, based on the adopted criteria, was 95.54%, and the accuracy of its validation was 90.74%. The relative loss value during the training process of the SB3D-NET model was 18.53%, and during its validation process, it was 37.76%. The proposed SB3D-NET neural network model for all twenty-two criteria achieves values of global error (GE) of prediction and classification of seeds at the level of 0.0992. Full article

This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning. Supervised regression for photovoltaic (PV) direct current power prediction was conducted using only sensor-based inputs (PanelTemp, Irradiance, AmbientTemp, Humidity), together with physically motivated-derived features (ΔT, IrradianceEff, IrradianceSq, Irradiance × ΔT). Samples acquired under very low irradiance (<50 W m⁻²) were excluded. Predictors were standardized with training-set statistics (z-score), and the target variable was modeled in log space to stabilize variance. A shallow artificial neural network (ANN; single hidden layer, widths {4–32}) was compared with deeper multilayer perceptrons (DNN; stacks {16 8}, {32 16}, {64 32}, {128 64}, {128 64 32}). Hyperparameters were selected with a grid search using validation mean squared error in log space with early stopping; Bayesian optimization was additionally applied to the ANN. Final models were retrained and evaluated on a held-out test set after inverse transformation to watts. Test performance was obtained as MSE, RMSE, MAE, R², and MAPE for the ANN and DNN. Hence, superiority in absolute/squared error and explained variance was exhibited by the ANN, whereas lower relative error was achieved by the DNN with a marginal MAE advantage. Ablation studies showed that moderate depth can be beneficial (e.g., two-layer variants), and a simple bootstrap ensemble improved robustness. In summary, the ANN demonstrated superior performance in terms of absolute-error accuracy, whereas the DNN exhibited better consistency with relative-error accuracy. Full article

Auricular avulsion injuries are rare, and microvascular reimplantation is considered the preferred treatment according to current literature. However, when a small skin pedicle is preserved, non-microvascular reattachment techniques may offer comparable outcomes. This systematic review aims to assess whether these techniques could represent a viable alternative. We analyzed 32 cases of pedicled auricular avulsion reported in 16 articles, focusing on patient demographics, injury mechanisms, pedicle characteristics, venous congestion, and postoperative management. Venous congestion occurred in 11 patients, with a significantly higher risk in narrower pedicles (mean width 9.82 mm; 95% CI: 4.75–14.89; p = 0.025). Prophylactic heparin significantly reduced this risk (p = 0.007). Other interventions—leech therapy and hyperbaric oxygen—lacked sufficient data for firm conclusions. Most cases achieved graft survival; necrosis occurred in some, and only two patients required additional surgery. Non-microvascular techniques appear to be a viable alternative to microvascular reimplantation, with similar results and potentially fewer complications. Venous congestion remains the main challenge, requiring active management and hospitalization for monitoring. Limited case series and publication bias still hinder the development of standardized guidelines. Full article

This paper presents a small-signal model of a series resonant converter under continuous conduction mode, based on asymmetric pulse-width modulation, which is commonly used under light-load conditions. When controlled using conventional pulse-frequency modulation, the series resonant converter (SRC) suffers from insufficient resonant current under light loads, leading to degraded soft-switching performance, increased switching losses, and reduced efficiency due to the need for higher switching frequencies to maintain output regulation. To address these issues, the asymmetric pulse-width modulation with a fixed switching frequency is required to improve efficiency. In this study, a small-signal model is derived using the Extended Describing Function method. Based on this model, transfer functions are obtained and verified through MATLAB(R2024a), switching model-based PLECS(4.7.5) simulations, and experimental results. Full article

The study evaluates the efficacy of an image-guided CAP treatment method with a plasma device capable of rapid biofilm removal from chicken tissue. The plasma treatment operating configuration includes a gas mixture of Argon and H₂O at a flowrate of 1.5 lpm. An X-Y stage was used to move the chicken sample below the stationary plasma scalpel at a speed of 0.1 mm/s. The discharge voltage and current were maintained between 3.2 and 3.7 kV (AC 20 kHz), and at 3 mA, respectively. The electrode gap and sample distance were set to 0.6 mm and 4 mm. This configuration facilitated effective biofilm removal, as confirmed by CFU analysis and 3D microscopic analysis showing a >99% reduction in biofilm post treatment with an etch rate of 2.2–5.8 µm/s and an impact width of up to 300 µm. The plasma scalpel electrode temperature reached 94.7 °C, while the targeted biofilm area was heated to 36.3 °C, suggesting non-thermal biofilm disruption. Three-dimensional microscopic analysis revealed biofilm thickness on chicken tissues ranging from 20 to 180 µm, comparable to biofilm loads on mammalian tissues. In conclusion, the study highlights the potential of CAP devices as a promising solution for biofilm debridement. Full article

This paper presents modeling for an external rotor permanent magnet generator (PMG) with fractional-slot concentrated windings working with a power electronic converter in the rotor magnetic field coordinate—the model is also called the DQ model. The model is needed to synthesize controllers of the PMG. Additionally, modeling for an active rectifier of the PMG is also investigated. The models of PMG and the active rectifier with two closed loops, namely the current loop and dc voltage loop, are verified by simulation in Matlab/Simulink. By modeling PMG in the rotor magnetic field coordinate, vector current can be decomposed in two independent currents, namely active current and reactive current. By controlling the active current, active power or electromagnetic torque or DC bus voltage can be controlled. By setting a relevant reactive current, the power factor or reactive power or rotor magnetic flux of PMG can be controlled. Simulation results of control PMG working with an active converter, such as pulse width modulation voltage, current, DC voltage, or power, are reported. The simulation helps to synthesize controllers and improve performances of the PMG working with the converter in wind applications. Full article

Medium-voltage energy storage converter equipment is an important component of the new generation of ship power and power systems. Virtual space vector pulse width modulation, as a modulation optimization method to improve the neutral-point voltage imbalance in medium- and high-voltage multilevel energy storage converters, has become a research hotspot for T-type three-level energy storage inverter modulation methods due to its significant balancing effect and simple implementation. However, the current research method of constructing virtual vectors through redundant small vectors has limitations in regulating the neutral-point potential under full (especially high) modulation ratios. This paper proposes a modulation method that uses hybrid variable virtual small vectors and virtual medium vectors through optimization selection and reconstruction of basic vectors. This method ensures that the neutral-point charge change of the vector is zero and the common-mode voltage is minimized within the switching period under the full modulation ratio, achieving the purpose of controlling the neutral-point voltage balance and suppressing the common-mode voltage. Finally, simulation and experimental results show that the proposed method has good neutral-point voltage regulation and common-mode voltage suppression capabilities within the full modulation ratio range, and the system also has strong robustness and adaptability under different load conditions. Full article

The Huangliu Formation, Section I, Gas Group II, at the eastern X gas field of the Yinggehai Basin, hosts thick, irregularly deposited sandstone bodies. The genesis of these sedimentary sand bodies has remained unclear. Utilizing drilling logs, core samples, and 3D seismic data from this field, this study integrates seismic geomorphology analysis, paleo-hydrodynamic reconstruction, and sedimentary numerical simulation to investigate the spatiotemporal evolution of the depositional system under micro-paleotopographic conditions during Gas Zone II sedimentation. Key conclusions include the development of seven morphologically diverse isolated sand bodies in the Lower II Gas Zone, covering areas of 1.4–13.4 km² with thicknesses ranging from 8.0 to 42.0 m. These sand bodies consist predominantly of massive fine-grained sandstone, characterized by box-shaped gamma-ray (GR) log responses and U- or V-shaped seismic reflection configurations. Reconstruction of paleo-turbidity current hydrodynamics for the Lower II depositional period was achieved through analysis of topographic slope gradients and the dimensional constraints (width/depth) of confined channels. Critically, slope gradients within the intraslope basin prompted a transition from supercritical to subcritical flow states within turbidity currents. This hydraulic transformation drove alternating erosion and deposition along the seafloor topography, ultimately generating the observed irregular, isolated turbidite sand bodies. Full article

Liriope muscari is a medicinal and ornamental herbaceous plant with significant economic value, as its tuberous roots are used for medicinal purposes. However, the current production of medicinal plants is characterized by wasteful use of resources and ecological risks caused by the unreasonable application of nitrogen fertilizers. In this study, based on uniform application of phosphorus and potassium fertilizers, six nitrogen application levels were set in pot experiments (expressed as N): N0: 0 kg/ha, N1: 208.33 kg/ha, N2: 416.66 kg/ha, N3: 625 kg/ha, N4: 833.33 kg/ha, N5: 1041.66 kg/ha). The morphological characteristics, photosynthetic physiology, tuber yield and quality, and seven nitrogen fertilizer utilization indices of L. muscari were analyzed and measured. Correlation analysis and structural equation modeling (SEM) were employed to investigate the mechanism by which nitrogen influences its growth and development, photosynthetic characteristics, tuber yield and quality, and nitrogen fertilizer utilization efficiency. The results showed that (1) nitrogen significantly promoted plant height, crown width, tiller number, and chlorophyll synthesis, with the N3 treatment (625 kg/ha) reaching the peak value, and the crown width and tiller number increasing by 26.44% and 38.90% compared to N0; the total chlorophyll content and net photosynthetic rate increased by 39.67% and 77.04%, respectively, compared to N0; high nitrogen (N5) inhibited photosynthesis and increased intercellular CO₂ concentration; (2) Fresh weight of tuberous roots, polysaccharide content, and saponin C content peaked at N3 (34.67 g/plant, 39.89%, and 0.21%), respectively, representing increases of 128.69%, 28.37%, and 33.66% compared to N0; (3) Nitrogen uptake, nitrogen fertilizer utilization efficiency, agronomic utilization efficiency, and apparent utilization efficiency were optimal at N3, while high nitrogen (N4–N5) reduced nitrogen fertilizer efficiency by 40–60%; (4) SEM analysis indicated that tiller number and transpiration rate directly drive yield, while stomatal conductance regulates saponin C synthesis. Under the experimental conditions, 625 kg/ha is the optimal nitrogen application rate balancing yield, quality, and nitrogen efficiency. Excessive nitrogen application (>833 kg/ha) induces photosynthetic inhibition and “luxury absorption”, leading to source-sink imbalance and reduced accumulation of secondary metabolites. This study provides a theoretical basis and technical support for the precise management of nitrogen in Liriope-type medicinal plants. It is expected to alleviate the contradictions of “high input, low output, and heavy pollution” in traditional fertilization models. Full article

Road extraction from remote sensing imagery plays a critical role in applications such as autonomous driving, urban planning, and infrastructure development. Although deep learning methods have achieved notable progress, current approaches still struggle with complex backgrounds, varying road widths, and strong texture interference, often leading to fragmented road predictions or the misclassification of background regions. Given that roads typically exhibit smooth low-frequency characteristics while background clutter tends to manifest in mid- and high-frequency ranges, incorporating frequency-domain information can enhance the model’s structural perception and discrimination capabilities. To address these challenges, we propose a novel frequency-aware road extraction network, termed PWFNet, which combines frequency-domain modeling with multi-scale feature enhancement. PWFNet comprises two key modules. First, the Pyramidal Wavelet Convolution (PWC) module employs multi-scale wavelet decomposition fused with localized convolution to accurately capture road structures across various spatial resolutions. Second, the Frequency-aware Adjustment Module (FAM) partitions the Fourier spectrum into multiple frequency bands and incorporates a spatial attention mechanism to strengthen low-frequency road responses while suppressing mid- and high-frequency background noise. By integrating complementary modeling from both spatial and frequency domains, PWFNet significantly improves road continuity, edge clarity, and robustness under complex conditions. Experiments on the DeepGlobe and CHN6-CUG road datasets demonstrate that PWFNet achieves IoU improvements of 3.8% and 1.25% over the best-performing baseline methods, respectively. In addition, we conducted cross-region transfer experiments by directly applying the trained model to remote sensing images from different geographic regions and at varying resolutions to assess its generalization capability. The results demonstrate that PWFNet maintains the continuity of main and branch roads and preserves edge details in these transfer scenarios, effectively reducing false positives and missed detections. This further validates its practicality and robustness in diverse real-world environments. Full article