Search Results (436)

When a fault occurs in the power grid to which the Virtual Synchronous Generator (VSG) is connected, it leads to overcurrent phenomena, which threatens the safety of the inverter and easily results in device damage. Although existing direct current limiting unit (CLU) control strategies can restrict the fault current, the input active power command far exceeds the power output, causing the virtual rotor to continuously accelerate. This leads to power angle divergence and a subsequent loss of synchronization. To address the conflict between direct current-limiting control and system transient stability, this paper proposes a control strategy based on the Particle Swarm Optimization (PSO) algorithm to modify the active power command, building upon existing direct current-limiting VSG control. During grid faults, the output current is constrained to its maximum value, leading to a reduction in the system’s output power. By leveraging the PSO algorithm, the proposed strategy decreases the active power command to minimize the power mismatch between the command and the output. This maximizes the system’s transient stability by minimizing the rotor acceleration torque and effectively suppressing excessive power angle deviation. Meanwhile, the active power command reduction is introduced as a penalty term to maximize the active power output capability during the fault period. Simulation results demonstrate that, compared to VSG with only direct current-limiting control, the proposed strategy significantly enhances the transient stability and transmission efficiency of the VSG under long-term fault conditions across various grid voltage sag scenarios. Furthermore, it ensures a seamless transition from the fault state to normal operation during short-term faults. Full article

The next generation of naval and defense systems will strain current naval ship cooling systems. Throughout its life-cycle, this strain will alter the behavior of the physical system, and any virtual representation of the system will become outdated due to component aging. Digital twins are a trending tool that can assimilate real-time sensor data to tailor a virtual representation to its physical counterpart. The online faithful virtual representation of the physical system provided by digital twins can be used for real-time system optimizations and proactive fault detection, diagnostics, and control adjustments, alleviating the stress of component aging. To support these complex power systems throughout their lifecycles, data-driven solutions for digital twin tuning will become essential. This paper investigates the application of a parameter-tuning digital twin framework to enhance the performance of a multi-physics model. The digital twin framework comprises a digital twin tuning scheme, a physical testbed designed to emulate the cooling system of a ship, and a multi-physics representation of that system. The digital twin tuning scheme leverages a swarm of particles and online sensor data to evaluate permutations of parameters to update the digital representation periodically. The digital twin framework was applied to a physical system to provide experimental data results demonstrating the usefulness of the tuning system. The physical system was designed and constructed to emulate the heat generation and dissipation from 6 liquid-cooled power converters under loads ranging from 10–15 kW at 99% efficiency. Two scenarios were applied to evaluate the performance of the digital twin framework. Results demonstrate that the digital twin framework can adapt to system changes in real-time and improve the accuracy of the related virtual representation by more than 90% when measured at four points of the system under test. Full article

This paper presents a geometry-driven framework for temporally stable 2D isoline reconstruction from particle-based simulation data. Unlike conventional Marching Squares methods, which assume grid-aligned scalar fields and often suffer from boundary jitter and flickering when applied to unstructured particle distributions, the proposed method constructs a continuous scalar field using an SPH kernel and estimates stabilized normals from level-set gradients at cell-level representative positions. Instead of relying on explicit Quadratic Error Function (QEF) optimization, we introduce a virtual-plane projection strategy that determines isoline vertices using a local geometric constraint. This projection can be interpreted as a first-order geometric approximation of QEF minimization, enabling QEF-free vertex positioning while reducing sensitivity to noisy particle-derived normals. As a result, the proposed method improves robustness in sparse particle regions while preserving important geometric features. To further enhance computational efficiency, we integrate a boundary-aware greedy meshing scheme that merges redundant interior geometry while preserving isoline boundaries. Experimental results demonstrate that the proposed method improves boundary stability and area consistency, reduces temporal variation, and decreases triangle counts by up to 70–75% compared with Marching Squares (MS) and Extended Marching Cube (EMC)-based reconstruction. These results indicate that the proposed framework is suitable for efficient real-time visualization of dynamic particle-based simulations. Full article

To address the voltage over-limit issue in rural distribution networks caused by high-penetration distributed photovoltaic (DPV) integration, as well as the frequent voltage disturbances resulting from frequent load switching and variable operating conditions in agricultural grid systems, this paper proposes a dual-layer optimized configuration and adaptive voltage control method for grid-forming energy storage systems. First, an outer-layer siting and sizing model is established with constraints including voltage stability, deviation, network losses, and economic factors. The configuration scheme is solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Then, an inner-layer rolling optimized dispatch model is constructed with Model Predictive Control (MPC) as the core, and an adaptive reactive power–voltage control method based on Virtual Synchronous Generator (VSG) control is proposed to enhance transient voltage support under disturbance conditions. Simulation analysis based on an actual 10 kV rural distribution line verifies that the proposed method can effectively mitigate overvoltage issues and alleviate reverse power flow during typical daily operation, while significantly improving node voltage recovery speed and reactive power support capability under voltage disturbances. Full article

Flood discharge observations in Japan are shifting from the conventional float-based methods to unmanned techniques such as radio-wave current meters. These approaches differ fundamentally in their measurement principles: the former is based on a Lagrangian framework, whereas the latter relies on a Eulerian framework. In this study, surface velocity fields obtained using particle image velocimetry (PIV) were used to track virtual tracers and derive Lagrangian surface velocities, providing a basis for examining the characteristics of Lagrangian and Eulerian measurements in an actual river under flood conditions. The uncertainties associated with the two frameworks were quantitatively compared, and the principal sources of uncertainty in Lagrangian measurements were identified. To achieve accurate discharge observation based on Eulerian measurements, the influences of measurement duration, subsection configuration, and vertical velocity distribution were investigated. The results suggest that measuring many points over a short duration is more effective than measuring a few points over a long duration. In a fixed-point measurement of subsurface velocity, a velocity dip was observed. Furthermore, the results quantitatively demonstrate the effects of bridge-pier wakes on the required averaging time and subsection configuration, highlighting the practical advantage of conducting observations on the upstream side of bridges. Full article

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization. Full article

In the parallel operation of islanded microgrids, line impedance mismatches and random load fluctuations, along with the dynamic response lag and difficulty in multidimensional parameter tuning of traditional control strategies, lead to power sharing imbalances and instability in frequency and voltage. To address these issues, this paper proposes a hierarchical cooperative control strategy based on consistency that integrates load-observation-based dynamic reference feedforward (LODRF) and adaptive particle swarm optimization (APSO). First, an improved adaptive virtual impedance (IAVI) strategy based on consistency is introduced into the virtual synchronous generator control framework. Second, an LODRF mechanism is applied at the secondary control layer to actively reconstruct the power baseline by observing the load status at the point of common coupling (PCC) in real time. Furthermore, an APSO algorithm utilizing the integral of time-weighted absolute error (ITAE) as a global performance index is constructed to optimize key proportional–integral controller parameters cooperatively. Simulation results from a four-unit heterogeneous parallel system in MATLAB/Simulink demonstrate that the IAVI strategy enables stable convergence of frequency and voltage and proportional power sharing. Compared with the system without LODRF, the proposed strategy reduces maximum frequency and voltage dynamic deviations under load disturbances by 78.5% and 53.3%, respectively, and shortens effective recovery times by 0.01 s and 0.09 s, respectively. Moreover, compared with the standard PSO algorithm, the APSO-optimized system reduces maximum frequency and voltage deviations by 3.1% and 36.4%, respectively. Additionally, average active and reactive power sharing errors in the steady state are kept below 0.9%, verifying the significant advantages of the strategy in improving dynamic disturbance rejection and steady-state precision. Full article

In quantum field theory, the vacuum is popularly considered to be a complex medium populated with virtual particle + antiparticle pairs. To an observer experiencing uniform acceleration, it is generally held that these virtual particles become real, appearing as a gas at a temperature that grows with the acceleration. This is the Unruh effect. However, it has been shown that vacuum complexity is an artifact produced by treating quantum field theory in a manner that does not manifestly enforce causality. Choosing a quantization approach that patently enforces causality, the quantum field theory vacuum is barren, bereft even of virtual particles. We show that acceleration has no effect on a trivial vacuum; hence, there is no Unruh effect in such a treatment of quantum field theory. Since the standard calculations suggesting an Unruh effect are formally consistent, insofar as they have been completed, there must be a canceling contribution that is omitted in the usual analyses. We argue that it is the dynamical action of conventional Lorentz transformations on the structure of an Unruh detector. Full article

Precise manipulation and directed transport of micro- and nano-particles are cornerstones of emerging lab-on-a-chip technologies. Traditional optofluidic systems that combine optical tweezers with microfluidic channels enable long-range transport. However, they rely on fixed physical boundaries that lack reconfigurability. To bridge this gap, we propose a reconfigurable virtual optical waveguide (VOW) based on a discretized beam-shaping strategy. By superposing two orthogonally polarized shaped beams, we construct interference-free optical channels without physical boundaries. This platform enables programmable transport along complex trajectories, including space-filling Hilbert curves that maximize interaction path length, and shields the transport channel from perturbations induced by surrounding particles. Crucially, the VOW offers multi-dimensional sorting capabilities: (i) it performs precise size-dependent sieving via tunable channel widths, and (ii) it functions as an intrinsic material filter by stably guiding scattering-dominated particles (e.g., gold) while rejecting gradient-dominated dielectric ones. This work establishes a versatile, contactless strategy for adaptive optical logistics and on-chip material purification. Full article

This paper presents an engineering framework for smart grid virtual power plant (VPP) day-ahead scheduling using fractional calculus-enhanced particle swarm optimization, targeting practical deployment in energy management systems. A fractional calculus-enhanced particle swarm optimization algorithm was developed and validated for day-ahead scheduling in virtual power plants using authentic market data and rigorous statistical analysis. The algorithm incorporates Grünwald–Letnikov fractional derivatives with adaptive memory into particle velocity updates, enabling trajectory-aware search that leverages historical exploration patterns. A factorial experiment across 500 independent test cases (50 dates × 10 trials) with controlled random seeds demonstrated that fractional particle swarm optimization increased mean daily profit by $205, representing a 4.1% improvement over standard particle swarm optimization. Wilcoxon signed-rank tests confirmed statistical significance (p < 0.0001, Cohen’s d = 1.08), with superior performance observed in 89.4% of cases. The factorial design identified fractional calculus as the primary performance driver, while advanced scenario generation provided no significant additional benefit. Sensitivity analysis indicated that wind generation variability was the primary predictor of performance variance, with profit difference standard deviations ranging from $34 to $325 depending on meteorological conditions, supporting the use of adaptive computational strategies. Computation required approximately two minutes per optimization on standard hardware. These findings establish fractional calculus as a credible enhancement for operational energy systems and demonstrate that the quality of optimization algorithms outweighs the complexity of forecast uncertainty modeling. The results extend fractional calculus applications from benchmark functions to practical infrastructure scheduling, with projected annual value exceeding $74,000 for a 50-megawatt system. The three-stage optimization architecture is designed for integration with standard energy management systems and SCADA platforms, offering a deployable pathway for smart grid operators. Full article

In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment. Full article

Rational design of high-performance viscosifying polymers is critical for enhancing supercritical CO₂ flooding efficiency in enhanced oil recovery (EOR). Traditional experimental and simulation approaches are limited in exploring the vast design space of polymer architecture, flexibility, and intermolecular interactions. This work presents an integrated machine learning (ML) and mesoscopic simulation framework using Dissipative Particle Dynamics (DPD) to accelerate the development of tailored polymeric thickeners. We systematically investigate synergistic effects of linear and branched polymer blends on solvent viscosity under Poiseuille flow, representative of flow in micro-fractures and pore throats. Key molecular descriptors are varied to generate a comprehensive rheological database. This data trains a deep neural network (DNN) surrogate model linking molecular parameters to macroscopic viscosity. The DNN is coupled with gradient ascent optimization for inverse design, enabling rapid virtual screening of thousands of formulations. A focused case study demonstrates that the star-like architectures with associative cores and semi-flexible backbones outperform linear analogs for supercritical CO₂ viscosity enhancement. The optimal candidate—a four-arm star polymer with linear side chains—was validated by DPD simulation. This multiscale “simulation-to-surrogate” methodology bridges molecular design with continuum-scale flow behavior, offering a transformative tool for formulating cost-effective, efficient, and sustainable next-generation EOR chemicals. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. The interaction is modeled as a Stackelberg–Nash equilibrium framework, in which OK, we will make the necessary revisions as per the requirements.a public energy storage operator and a natural gas company act as leaders to maximize social welfare and design differentiated trading strategies for VPPs. The VPPs act as followers and participate in cooperative energy trading based on a generalized Nash equilibrium scheme, sharing surplus energy and allocating cooperative benefits according to their contributions. To address uncertainty, Conditional Value at Risk (CVaR) is adopted to quantify the expected loss of the upper-level decision makers. The lower-level VPP problem is formulated as a three-stage stochastic robust optimization model considering renewable generation uncertainty. To solve the resulting nonlinear bi-level problem, a two-stage solution approach combining particle swarm optimization and KKT-based reformulation is developed to transform it into a tractable mixed-integer linear programming model. Numerical case studies verify the effectiveness of the proposed framework. Full article

Against the backdrop of a low-carbon economy, the control of soot emissions from combustion processes is of paramount importance. In this study, the effects of CO₂ dilution on soot formation in ethylene laminar diffusion flames are investigated through a combination of experimental measurements and numerical simulations. In addition, a virtual species, denoted as F_xCO₂, is introduced to progressively decouple the individual mechanisms by which different effects suppress soot formation. The results indicate that increasing the CO₂/N₂ dilution ratio leads to reductions in both the peak flame temperature and the soot volume fraction, with CO₂ exhibiting a more pronounced inhibitory effect than N₂. The decoupling analysis reveals that the dilution effect and the chemical effect are the dominant contributors to flame temperature reduction. The soot-inhibiting effectiveness of the individual effects follows the order: dilution effect > thermal effect > chemical effect > density effect > transport effect. With respect to their influence on C₂H₂ concentration, the effects are ranked as: dilution effect > chemical effect > transport effect > thermal effect > density effect. The chemical effect suppresses the formation of OH radicals, thereby reducing the flame temperature and H radical concentration. In contrast, the dilution effect enhances soot oxidation by increasing the OH radical concentration, effectively inhibiting soot particle formation. Full article