Search Results (4,783)

This article proposes a novel three-dimensional trench electrode detector, named the through-type three-dimensional quasi-hemispherical electrode detector. The detector adopts a trench structure to package each independent unit and achieves complete penetration of trench electrodes with the help of an SOI substrate. The horizontal distances from the center anode of the detector to the trench cathode and the detector thickness are equal. It has a near-spherical structure and exhibits spherical-like electrical performance. In this study, we modeled the device physics of the new structure and conducted a systematic three-dimensional simulation of its electrical characteristics, including the electric field, electric potential, electron concentration distribution of the detector, the inducted current caused by incident ions, and the crosstalk between detector units. Computational and technology computer-aided design (TCAD) simulation results show that the detector has an ultra-small capacitance (2.7 fF), low depletion voltage (1.4 V), and uniform electric field distribution. The trench electrodes electrically isolate the pixel units from each other so that the coherence effect between the units is small and can be applied in high-resolution X-ray photon counting detectors to enhance the contrast-to-noise ratio of low-dose imaging and the detection rate of tiny structures, among other things. Full article

This paper presents a novel methodology for the optimal sizing of solar photovoltaic (PV) systems in distribution networks by determining the monthly optimum tilt and azimuth angles to maximize solar energy capture. Using one year of solar irradiation data, the Grey Wolf Optimizer (GWO) is employed to optimize the tilt and azimuth angles with the objective of maximizing monthly solar insolation. Unlike existing approaches that assume fixed azimuth angles, the proposed method calculates both tilt and azimuth angles for each month, allowing for a more precise alignment with solar trajectories. The optimized orientation parameters are subsequently utilized to determine the optimal number and placement of PV panels, as well as the optimal location and sizing of shunt capacitor (SC) banks, for the IEEE 69-bus distribution system. This optimization is performed under peak load conditions using the GWO, with the objectives of minimizing active power losses, enhancing voltage profile stability, and maximizing PV system penetration. The long-term impact of this approach is assessed through a 20-year energy and economic savings analysis, demonstrating substantial improvements in energy efficiency and cost-effectiveness. Full article

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with $k-5$ and $k-10$ nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), Power Factor ($PF$), and Power Load ($PL$). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for $TH{D}_u$, $TH{D}_i$, and $PF$. Only BODT obtained a good forecasting result for $PL$. The KNN (k = 5) algorithm obtained a good result for $PF$ prediction. The KNN (k = 10) algorithm predicted acceptable results for U and $PF$. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees. Full article

The optimal allocation of Wireless Smart Concentrators (WSCs) in low-voltage (LV) distribution networks poses significant challenges due to signal attenuation caused by varying building densities and vegetation. This paper proposes a Variable Neighborhood Search (VNS) algorithm to optimize the placement of WSCs in LV distribution networks. To comprehensively assess the proposed approach, both linear and nonlinear mathematical formulations are considered, depending on whether the distance between meters and concentrators is treated as a fixed parameter or as a decision variable. The performance of the proposed VNS algorithm is benchmarked against both exact solvers and metaheuristics such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Tabu Search (TS). In the linear formulation, VNS achieved the exact optimal solution with execution times up to 75% faster than competing methods. For the more complex nonlinear model, VNS consistently identified superior solutions while requiring less computational effort. These results underscore the algorithm’s ability to balance solution quality and efficiency, making it particularly well-suited for large-scale, resource-constrained utility planning. Full article

High shares of photovoltaic energy in low-voltage distribution systems lead to voltage limit violations. Deploying energy storage systems in the network can compensate for the mismatch between the generation and the consumption; nevertheless, the mismatch is unevenly distributed throughout the network, suggesting aggregated control strategies as a solution. This paper proposes two coordination control strategies of batteries to address network overvoltage conditions caused by high penetration of photovoltaic systems. The leader–follower coordination strategy determines a battery’s utilization factor by using the node closest to a voltage violation as a reference. The leaderless control uses a shared utilization factor to avoid excessive usage of a particular agent in the network. We tested both approaches in the 18-node CIGRE network for scenarios when not all agents were available and when they had different starting states-of-charge. Our results demonstrate that both strategies are capable of voltage control; however, the leader–follower control leads to uneven storage usage, ultimately leading to short-time failure to comply with the voltage limits under extreme conditions where neighbouring agents must compensate for the unavailable one. Conversely, the leaderless approach presents more balanced use of the agents thanks to the distributed utilization factor, resulting in a more robust control strategy. Full article

This paper presents a novel electro-thermal modeling approach for a lithium-ion battery pack in an electric vehicle (EV), along with parameter identification using controller area network (CAN) data collected from chassis dynamometer and real-world driving tests. The proposed electro-thermal model consists of a first-order equivalent circuit model (ECM) and a lumped-parameter thermal network in considering a simplified cooling circuit layout and temperature distributions across four distinct zones within the battery pack. This model captures the nonuniform heat transfer between the pack modules and the coolant, as well as variations in coolant temperature and flow rates. Model parameters are identified directly from vehicle-level test data without relying on laboratory-level measurements. Validation results demonstrate that the model can predict terminal voltage with an RMSE of less than 6 V (normalized root mean square error of less than 2%), and battery module surface temperatures with root mean square errors of less than 2 °C for over 90% of the test cases. The proposed approach provides a cost-effective and accurate solution for predicting electro-thermal behavior of EV battery systems, making it a valuable tool for battery design and management to optimize performance and ensure the safety of EVs. Full article

This study investigates the anodization behavior and surface modification of Ti6Al4V (Ti64) alloy components fabricated via electron beam powder bed fusion (EB-PBF), aiming to enhance their performance in biomedical applications. Ti64 samples were manufactured using optimized EB-PBF parameters to produce a uniform microstructure and surface quality. Electrochemical anodization at 40 V and 60 V for 2 h generated self-organized TiO₂ nanotube layers, followed by a heat treatment at 550 °C to improve crystallinity while preserving the nanotube morphology. Characterization using scanning electron microscopy (SEM) and atomic force microscopy (AFM) revealed that a lower voltage produced uniform, compact nanotubes with moderate roughness and higher hardness, whereas a higher voltage generated thicker, less ordered nanotubes with larger diameters, increased roughness, and slightly reduced mechanical performance. X-ray diffraction (XRD) confirmed the presence of anatase TiO₂ phases, and energy-dispersive spectroscopy (EDS) analysis revealed a homogeneous distribution of Ti and O. Mechanical testing via nanoindentation and nanoscratch techniques demonstrated superior hardness and adhesion in nanotubes formed at lower voltage due to their compact structure. Electrochemical measurements indicated significantly enhanced corrosion resistance in anodized samples, attributed to the dense and chemically stable TiO₂ layer that acts as a barrier to aggressive ions and reduces active corrosion sites. In vitro bioactivity analysis further confirmed improved apatite formation on anodized surfaces. These results demonstrate the synergistic potential of EB-PBF and controlled anodization for modifying the surface properties of Ti64 implants, leading to improved mechanical behavior, corrosion resistance, and biological performance suitable for biomedical applications. Full article

Power flow analysis of low-voltage network (LVN) is one of the most crucial methods for achieving refined management of such networks. To accurately calculate the three-phase (TP) probabilistic power flow (PPF) distribution in LVN, this paper first draws on the injection-type Newton method; by leveraging TP power measurements relative to the neutral point obtained from smart meters, the injected power is expressed in terms of injected current equations, thereby establishing TP power flow models for various components within the low-voltage distribution transformer area grid. Subsequently, addressing the stochastic fluctuation models of load power and photovoltaic output, this paper employs conventional numerical methods and an improved Latin hypercube sampling technique. Utilizing linearized power flow equations and based on the improved semi-invariant method (SIM) and Gram–Charlier (GC) series fitting, a calculation method for three-phase PPF in low-voltage distribution transformer area grids using the improved semi-invariant is proposed. Finally, simulations of the proposed three-phase PPF method are conducted using the IEEE-13 node distribution system. The simulation results demonstrate that the proposed method can effectively perform three-phase PPF calculations for the distribution transformer area grid and accurately obtain probabilistic distribution information of the TP power flow within the grid. Full article

In recent years, arresters in 10 kV distribution transformer areas of the Guangdong power grid have exhibited a rising trend of premature failures, posing a serious threat to distribution network reliability. This paper studied the failure causes of arresters through performance tests on failed arresters and through deterioration tests on new arresters and their prorated sections under typical operating stresses. The failed arresters and their internal varistors displayed varying degrees of physical damage and pronounced degradation in electrical performance, characterized by a strong polarity effect on the DC reference voltage (U_1mA), elevated DC leakage current (I_L) and resistive current (i_R), and excessive residual voltage (U_5kV). In the lightning impulse test, varistors primarily showed pinhole-type damage and significant polarity effects on ΔU_1mA. In the AC aging test, ΔU_5kV increased markedly. In the water immersion test, varistors exhibited salt deposits and aluminum electrode blackening, with ΔU_1mA decreasing, while I_L and Δi_R increased significantly. Overall, internal moisture superimposed on other operating stresses was identified as a major internal cause of arrester failure, while pollution flashover of the housing was considered the primary external factor. Corresponding improvement measures in material optimization, testing and inspection, and operation and maintenance are proposed to enhance arrester reliability. Full article

This paper presents an exhaustive search approach to determine the optimal placement of a Unified Power Quality Conditioner (UPQC) in a distribution system that integrates a distributed generation (DG) unit based on photovoltaic (PV) panels. The main objective is to enhance voltage profiles and reduce total harmonic distortion (THD) in the presence of nonlinear loads. A multi-objective optimization model is formulated, combining THD minimization and voltage deviation reduction through a weighted cost function. Two case studies are conducted using the IEEE 33-bus test system modeled in MATLAB Simulink, considering different scenarios: one with nonlinear loads and another with additional DG integration. The UPQC is tested at critical nodes to assess its impact on power quality indicators. Results show that placing the UPQC at node 14 yields the lowest cost function value in both cases, with THD reductions exceeding 90% at the installation node and notable improvements across the system. These findings confirm that brute-force optimization is a reliable and effective strategy for UPQC siting, especially in distribution networks subjected to nonlinear disturbances and renewable-based DG. The proposed methodology provides a practical framework for power quality enhancement and supports decision-making in modern smart grid environments. Full article

Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks. Full article

Low-voltage distribution area is the “last kilometer” connecting the distribution network and users, and the traditional distribution system is difficult to digitally manage in the low-voltage area, resulting in untimely and imprecise handling of voltage overruns, short-circuit outages, and other abnormal problems. With the deployment of smart meters, new sensors, smart gateways, and other devices in distribution areas, digital intelligent monitoring and management based on digital twins in LV distribution areas has gradually become the focus of distribution network research. In view of the profound changes that are taking place in the low-voltage distribution area, this paper first summarizes the characteristics and shortcomings of the existing digital twin research in the low-voltage distribution area, then puts forward the ‘6C’ development goals for the digital transformation of the low-voltage distribution area, introduces the practice work of Guangxi Power Grid Corporation around the ‘6C’ development goals in the low-voltage distribution area. Finally, the future research work of the ‘6C’ development goals for the digital transformation of the low-voltage distribution area is promising. Full article

The subject of this paper is how certain electrode array parameters affect the operating characteristics of electrohydrodynamic (EHD) propulsion systems. The focus is on how changes in the shapes and arrangements of electrodes, such as the diameter of the coronating conductor, effective electrode length and the spacing between electrodes, influence the formation and behaviour of the corona discharge and the resulting ion-induced airflow. A modular experimental setup was created to allow for a systematic study of each parameter in controlled atmospheric conditions using a high-voltage DC power supply. The study includes both the theoretical background and experimental methods, in order to explore the connections between the electric field distribution, ion mobility and propulsion force generation. By measuring the current, voltage and flow velocity, the impacts of design changes on the propulsion behaviour are examined. The findings help to improve the understanding of EHD propulsion mechanics and lay the groundwork for optimising electrode designs in future applications. This research supports the ongoing work to create compact, quiet and efficient propulsion technologies for use in lightweight aerial vehicles, precise fluid control and other engineering areas, where solid-state thrust systems have clear benefits over traditional methods. Full article

In distribution networks, single-phase ground faults often lead to abnormal changes in voltage and current signals. Traditional single-modal fault diagnosis methods usually struggle to accurately identify the fault line under such conditions. To address this issue, this paper proposes a fault line identification method based on a multimodal feature fusion model. The approach combines time-frequency images—generated using a Short-Time Fourier Transform (STFT) and Wigner–Ville Distribution (WVD) fusion algorithm with one-dimensional time-series signals for classification. The time-frequency images visualize both temporal and spectral features of the signal and are processed using the RepLKNet model for deep feature extraction. Meanwhile, the raw one-dimensional time-series signals preserve the original temporal dependencies and are analyzed using a BiGRU network enhanced with a global attention mechanism to improve feature representation. Finally, features from both modalities are extracted in parallel and fused to achieve accurate fault line identification. Experimental results demonstrate that the proposed method effectively leverages the complementary nature of multimodal data and shows strong robustness in the presence of noise interference. Full article

Anthropomorphic CT phantoms are essential training tools for interventional radiology. Given the high technical demands and stringent safety requirements in this field, realistic CT phantoms are vital simulation tools that support effective hands-on training, procedural planning, and risk mitigation. However, commercially available phantom geometries are limited in their scope. This study investigates the use of polyvinyl alcohol (PVA) to fabricate customizable training phantoms. PVA, a non-toxic material, can be processed into PVA cryogels (PVA-C) with tissue-like mechanical properties. We modified PVA-C (10 wt.% PVA) by incorporating various additives to adjust X-ray attenuation and achieve Hounsfield units (HUs) similar to different soft tissues. HU values were measured at X-ray tube voltages of 70, 120, and 150 kV. The inclusion of barium sulfate (e.g., U = 120 kV; 0.1–2 wt.%: 33.29 ± 5.45–323.72 ± 12.64 HU) and iohexol (e.g., U = 120 kV; 0.1–2 wt.%: 26.05 ± 4.74–161.99 ± 5.69 HU) significantly increased HU values. Iohexol produced more homogeneous HU distributions than barium sulfate and cellulose derivatives, with the latter having air gaps and inconsistencies. The tested formulations encompassed a wide range of soft tissue densities, with HU values varying significantly across the energy range (p < 0.001). While cellulose derivatives showed variable HU modulation, their primary role appears to be in modifying phantom texture and morphology rather than precise attenuation control. In conclusion, PVA-C demonstrates strong potential for use in interventional radiology training phantoms. Further studies may enhance phantom realism by replicating tissue textures, for example, through the incorporation of cellulose-based substances. Full article