Search Results (2,993)

In response to the frequent safety incidents associated with the core electrical systems (i.e., traction battery, charging system, and drive motor) of new energy vehicles (NEVs) and the lack of forward-looking quantitative risk assessment methods in existing detection and diagnostic technologies, this study introduces the Layer of Protection Analysis (LOPA) methodology into the field of NEV safety. Unlike qualitative methods (e.g., FMEA, FTA) or purely data-driven diagnosis, this work establishes a tailored semi-quantitative LOPA framework that defines scenario-specific independent protection layer (IPL) identification criteria and probability of failure on demand (PFD) assignment rules for NEV applications. Typical risk scenarios, including battery thermal runaway, electrical faults in charging systems, overheating of drive motors, and battery internal short circuits caused by mechanical abuse, are systematically analyzed in terms of their failure mechanisms and evolution processes. A tailored quantitative risk assessment framework is established and applied to conduct full-process risk evaluations for the four scenarios. The results indicate that, under the synergistic effect of multiple protection layers—including inherently safe design, basic process control systems, safety instrumented systems, and physical protection measures—the accident consequence frequencies of all scenarios are significantly lower than the tolerable risk thresholds. This verifies the applicability and effectiveness of the LOPA method in NEV safety analysis. The proposed quantitative framework provides a scientific basis for safety design optimization, identification of critical protective elements, and operation and maintenance strategy formulation throughout the lifecycle of NEVs. Furthermore, the limitations of data portability from process industries are discussed, and sensitivity analyses are conducted to confirm the robustness of the conclusions. Full article

The increasing penetration of inverter-based distributed energy resources (DERs) within distribution networks has resulted in harmonic distortion risks that can affect transformer thermal loading, service life, and network hosting capacity. This study assesses harmonic behaviour under increasing DER penetration using a detailed MATLAB 2025b/Simulink model of the CIGRÉ low-voltage benchmark feeder, adapted to reflect representative network parameters and run at a 400 V point of common coupling (PCC). DER penetration is incrementally increased from 0% to 195% of feeder load, and for each penetration level the PCC currents and voltages are examined using FFT-based spectrum extraction. The short-circuit strength is first calculated (I_SC/I_L = 14.1), and harmonic current and voltage distortion thresholds are benchmarked against IEEE 519:2022 and NRS 048-2:2025 respectively. Results show that while DER inverters introduce increasing odd-order harmonics, mainly the 3rd, 5th, 7th and 11th, the feeder’s moderate short-circuit capacity suppresses PCC voltage distortion, keeping voltage THD below 3% across all scenarios. As the inverter-based DER penetration increases, so does the harmonic current distortion. At 180%, Total Demand Distortion (TDD) nears the IEEE limit of 5%. Full article

A broadband method for measuring the complex permeability of thin magnetic films using a short-circuited microstrip fixture is presented. The method is based on full one-port offset-short calibration implemented directly in the fixture with a movable shorting wall, reducing sensitivity to coaxial-to-strip transition imperfections and eliminating the need for its precise optimization. Field-dependent normalization to the empty fixture reduces systematic errors from the external magnetic field, and a correction for residual saturated permeability improves retrieval accuracy. The method was validated on Co, supermalloy, and FeCo films on flexible PET substrates. Retrieved spectra agreed well with reference coaxial data and with the spectrum reconstructed from static magnetic measurements. In the present implementation, broadband spectra were obtained from 0.1 to 20 GHz with no significant systematic distortion, indicating that the proposed approach is suitable for accurate broadband characterization of thin magnetic films without reference standards or precise optimization of the coaxial-to-strip transition. Full article

High-voltage short pulse generators play a critical role in medical and industrial applications. However, the presence of residual stored energy can pose significant electrical safety hazards. To mitigate these hazards, the implementation of rapid discharge mechanisms is imperative. To address the limitations of slow passive bleeders and auxiliary-dependent active circuits, and the issue of excessive size for compact pulse generators, this study proposes a self-powered, fast-response discharge topology utilizing cascaded depletion-mode NMOS transistors. The method utilizes the inherent normally-on characteristic of depletion-mode devices to ensure fail-safe activation during power loss, employing a self-biased feedback loop to regulate a constant discharge current. The theoretical models were validated through simulations and a hardware prototype testing a 1200 V/220 nF capacitor. The experimental results demonstrate the capability to successfully discharge 1200 V to a safe level within a span of one second. Additionally, the discharge time can be programmed within the range from 72 milliseconds to 1.02 s by adjusting the current-limiting resistor. In summary, the proposed topology offers a reliable, compact, and adjustable solution for high-voltage safety, addressing the limitations of conventional discharge technologies in terms of volume and speed. Full article

Hydraulic short-circuit (HSC) has gained widespread attention as a novel approach to enhancing the flexibility of pumped-storage power plants (PSPPs). This paper investigates the flow and structural characteristics of bifurcated pipes in PSPPs, conducting numerical simulations under multiple operating conditions under pumping, generating, and HSC modes. Computational fluid dynamics (CFD) simulations indicate that the flow pattern deteriorates significantly under the HSC mode, with energy loss increasing notably as the flow split ratio (FSR) rises, though peaking at only 1.2% of total energy. Driven by secondary flow, a pair of counter-rotating Dean vortices develops from the upstream main pipe to the generating branch as the FSR increases. The entropy production rate reveals the energy dissipation mechanisms in the main flow region, namely, the shear interaction between high-velocity outflow and low-velocity vortex flow, along with the viscous dissipation within the Dean vortices. Furthermore, fluid–structure interaction (FSI) simulation results confirm that the structural reliability of the bifurcated pipe is ensured under the HSC mode, as the dominant load stems from the high static pressure of the upstream reservoir, with fluid impact loads playing a relatively insignificant role. This study provides a theoretical foundation for the practical operation of hydraulic short-circuit with respect to the performance and safety of a bifurcated pipe. Full article

Virtual synchronous generators (VSGs) are prone to transient power angle instability and short-circuit current overshoot under symmetrical short-circuit grid faults. To address the limitation that existing transient control strategies fail to simultaneously guarantee power angle stability and fault current limiting, a coordinated control strategy combining dynamic active power reference regulation and adaptive virtual impedance is designed. Specifically, the active power reference is dynamically adjusted in accordance with the voltage sag magnitude at the point of common coupling (PCC), which effectively narrows the acceleration area of the virtual rotor and maintains the transient power angle near its rated value to prevent the risk of system loss of synchronism. On this basis, an adaptive virtual impedance control scheme is designed to accurately calculate and implement the optimal current-limiting impedance on demand, confining the steady-state fault current within the allowable threshold. Finally, the effectiveness of the designed strategy is verified on the Matlab/Simulink simulation platform. Simulation results demonstrate that the designed strategy achieves the coordination between transient power angle stability and fault current limiting, thus improving the operational stability of the VSG grid-connected system under symmetrical short-circuit grid faults. Full article

Photovoltaic (PV) module faults, such as hotspots, diode short circuits, occlusions, and shadows, degrade power generation efficiency and safety. Existing manual inspection and single-modality methods show limited robustness under complex conditions, especially with illumination variations and weak thermal responses, while most deep learning approaches fail to exploit the complementarity of visible and infrared modalities. To address this issue, a dual-modality visible–infrared fusion framework based on YOLO11 is proposed, integrating a multi-scale pyramid pooling and dilated convolution module (MSPPD), a gradient-aware fusion module (GAFusion), and a dynamic convolution and element-wise scaling detection head (Detect-DEhead). GAFusion enhances cross-modal structural consistency and reduces feature misalignment and information loss during fusion by introducing gradient-aware feature interaction. Shape-IoU loss is employed to improve localization accuracy. The proposed method improves mean average precision (mAP)@0.5 from 86.7% to 88.1%, while reducing parameters, computational cost, and model size from 4.3 M to 3.7 M, 11.42 GFLOPs to 9.37 GFLOPs, and 9.1 MB to 7.9 MB, respectively. With Shape-IoU, mAP@0.5 reaches 88.4%, and recall increases from 78.5% to 84.9%. Experiments on the FLIR Thermal dataset achieve gains of 2.2%, 1.6%, and 2.7% in precision, recall, and mAP@0.5. The method achieves an effective trade-off between accuracy and efficiency for intelligent PV module inspection. Full article

Organic solar cells (OSCs) have emerged as a competitive alternative to conventional silicon-based photovoltaics for their inexpensive production, versatility, and reduced energy consumption. However, it is still challenging to accurately assess their performance due to the complex interactions between molecular structure and device-level features. We provide a physics-constrained graph neural network (GNN) architecture for multi-output prediction of key OSC parameters, including power conversion efficiency (PCE), open-circuit voltage, short-circuit current density, and fill factor in this study. To ensure agreement between the anticipated PCE and its physically derived formulation, a physics-guided regularization term is added. Experimental results on a dataset of 5628 samples show that the neural-only GNN achieves strong predictive performance ($R^2=0.630$), outperforming the baseline model random forest ($R^2=0.537$). The proposed physics-constrained GNN maintains comparable accuracy ($R^2=0.626$) while significantly reducing physics violation (from 0.406 to 0.104). These results show that adding physics constraints makes predictions more consistent without lowering accuracy, making it a reliable way to predict OSC performance. Full article

Dust accumulation on photovoltaic (PV) modules reduces power generation efficiency, and traditional water-based cleaning is impractical in arid regions. Inspired by the classical acoustic phenomenon of Chladni figures—specifically the mechanism where an acoustic standing wave field drives the regular migration and accumulation of particles—this study proposes a waterless dust removal method using low-frequency ultrasonic vibration via piezoelectric excitation. Impedance analysis identifies optimal electromechanical coupling at 28 kHz. Experiments demonstrate that higher driving voltages accelerate cleaning, with recovery rates saturating beyond 125 V. Notably, intense friction and collisions between particles within high-density dust layers consume substantial kinetic energy, significantly multiplying the required cleaning time. Macroscopic transport analysis reveals that dust removal relies on the synergy of vibration-induced adhesion decoupling and gravity-driven transport. Sufficient tangential gravity is crucial for macroscopic particle removal, and tilt angles above 30° provide the necessary downward driving force to ensure smooth particle sliding. Under optimal conditions, the system achieves an over 97% short-circuit current recovery at a low power consumption of ~10 W, providing a theoretical basis for waterless PV self-cleaning systems. Full article

This paper proposes an experimental methodology for the systematic determination of the equivalent circuit parameters of three winding high frequency transformers (3W-HFTs) for modeling the electrical behavior and the power losses of triple active bridge (TAB) power converters intended for onboard electric vehicle charging applications. For modeling the 3W-HFTs, a comprehensive lumped element equivalent circuit is adopted, and its electrical and electromagnetic parameters are determined through a structured sequence of open-circuit and short-circuit measurements performed over a wide frequency range from 20 Hz to 13 MHz using a precision impedance analyzer to thoroughly investigate impedance resonance behavior, while wide-bandgap power electronic devices are employed. The comparison between the lumped element impedance model and the measured impedance responses demonstrates strong agreement in terms of both magnitude and phase across the frequency range under study. Furthermore, the comparison of simulation results and experimental measurements performed on a TAB prototype under both open-circuit and load operating conditions validates the 3W-HFT electrical characteristics and the estimation of TAB’s power losses distribution. The close consistency between experimental results and simulation outcomes confirms the effectiveness of the proposed characterization approach. Full article

Renovating the grid structure by converting existing transmission lines into VSC-HVDC transmission lines can address issues such as limited transmission capacity and excessive short-circuit current in load-concentrated areas. To effectively evaluate the effectiveness of grid structure renovation and provide a reference for selecting suitable renovation sites, this paper proposes a comprehensive evaluation method for assessing the effectiveness of grid structure renovation. Firstly, an evaluation indicator system is constructed from four aspects. Then, the Fuzzy Analytic Hierarchy Process (FAHP) and Entropy Weight Method (EWM) are used to determine the subjective weight and objective weight of each indicator, and a game theory-based combined weighting method is applied to obtain the combined weight, which is then used to calculate the comprehensive evaluation value before and after renovation to reflect the effectiveness of the renovation. Subsequently, the TOPSIS method is employed for comparative verification of the evaluation method’s validity, and a sensitivity analysis is conducted on the subjective weight to confirm the method’s robustness to subjective preference. Finally, based on the indicator data obtained from the PSD-BPA simulation, the effectiveness of renovating eight scenarios in a provincial power grid is evaluated. The results show that grid structure renovation can enhance power grid performance in load centers. Full article

To address the increasingly prominent challenges of “low inertia” and “weak damping” in modern power systems, grid-forming (GFM) control technologies with inertia and damping support capabilities are being extensively adopted. However, distributed generation units interfaced with GFM inverters are highly susceptible to overcurrent phenomena during grid short-circuit faults. Existing research primarily focuses on current-limiting control strategies for virtual synchronous generators (VSGs), while investigations into their fault current characteristics remain insufficient. Given this, this paper proposes a short-circuit current calculation methodology for VSG-based PV-storage grid-connected systems. First, a model of a grid-forming PV-storage grid-connected system based on virtual synchronous control is established. Subsequently, the virtual impedance is solved within the timescale of current inner-loop stabilization, and the virtual internal electromotive force (EMF) equation for the VSG is formulated. This leads to the derivation of an analytical expression for the VSG short-circuit current, accounting for variations in the virtual internal potential. Furthermore, the impacts of diverse control parameters and fault severities on the short-circuit current are investigated based on this expression. Finally, simulations are conducted on the MATLAB/Simulink(R2024b) platform to validate the accuracy of the proposed short-circuit current calculation method and the correctness of the analysis regarding the influencing factors. Full article

Deep ConvNets suffer from gradient signal degradation as network depth increases, limiting effective feature learning in complex architectures. ResNet addressed this through residual connections, but these fixed short circuits cannot adapt to varying input complexity or selectively emphasize task-relevant features across network hierarchies. This study introduces GradAttn, a variation of the residual approach in CNNs that replaces the fixed residual connections with attention-controlled gradient flow. By extracting multi-scale CNN features at different depths and regulating them through self-attention, GradAttn dynamically weights shallow texture features and deep semantic representations. For representational analysis, we evaluated three GradAttn variants across eight diverse datasets: from natural images and medical imaging to fashion recognition. The results demonstrate that GradAttn outperforms ResNet-18 on five of eight datasets, achieving up to $+11.07\%$ accuracy improvement on FashionMNIST while maintaining a comparable network size. Gradient flow analysis reveals that controlled instabilities, introduced by attention, often coincide with improved generalization, challenging the assumption that perfect stability is optimal. Furthermore, positional encoding’s effectiveness turned out to be dataset-dependent, with CNN hierarchies frequently encoding sufficient spatial structure. These findings render attention mechanisms as enablers of learnable gradient control, offering a new way for adaptive representation learning in deep neural architectures. Full article

To address the power optimization problem of LCC-HVDC systems in multi-infeed receiving-end grids under system strength constraints, this paper systematically analyzes the influence mechanism of AC system strength on conventional DC transmission power, clarifying the quantitative relationship between the critical short circuit ratio and the system’s power transmission limit. A novel day-ahead power optimization method for multiple DC links is proposed, incorporating operational constraints such as frequency stability and voltage stiffness. Empirical simulation analysis of the Chinese Zhejiang Power Grid under a low-voltage typical operation mode in the summer of 2025 demonstrates that the optimized DC power transmission scheme significantly improves the system’s frequency response and voltage recovery characteristics under fault conditions, enhancing the overall security and stability level of the multi-infeed HVDC receiving-end grid. This research holds significant reference value for practical engineering applications. Full article

Metallic contamination is a critical manufacturing defect in lithium-ion batteries, but the degradation evolution and electrochemical signatures of Cu-contaminated cells remain insufficiently understood. In this study, Cu particles were intentionally introduced into graphite/NCM811 pouch cells to investigate Cu-induced internal short circuit, cycling degradation, and defect detection. The Cu-contaminated cells exhibit significantly higher initial self-discharge rates, indicating the formation of a cathode-to-anode type internal short circuit. X-ray microscopy and SEM/EDS characterization reveal local separator penetration, electrode deformation, Cu dissolution/migration/deposition, Al current collector dissolution, and deposit accumulation on the anode surface. After cycling, the Cu-contaminated cells showed accelerated capacity fade and increased direct current internal resistance, while their self-discharge rate gradually decreased, suggesting partial mitigation of the internal short circuit path. Incremental capacity analysis was used to evaluate the internal short circuit severity, while differential voltage analysis was further applied to distinguish a Cu-induced internal short circuit from normal aging. This work provides mechanistic insight into Cu-contamination-induced degradation and electrochemical signatures for identifying metallic-contamination defects in lithium-ion cells. Full article