Search Results (1,484)

Parallel grid-forming energy storage converters based on virtual synchronous generator (VSG) control are prone to active power oscillation and interphase circulating current under load disturbance, unit switching, and parameter mismatch conditions. To address these problems, this paper proposes a dual-layer damping control strategy that combines adaptive virtual damping in the power loop with capacitor current feedback damping in the current loop. First, the small-signal models of the LCL filter, VSG power loop, and parallel converter system are established, and the dominant oscillation modes are analyzed using eigenvalue and participation factor methods. Then, an adaptive damping coefficient is designed according to the active power deviation and frequency dynamic response to suppress low-frequency power oscillation, while a capacitor current feedback branch is introduced to reshape the LCL filter’s resonant poles and attenuate circulating current resonance. Compared with the conventional fixed-damping VSG control, the proposed method reduces active power overshoot and accelerates power redistribution under load step and unit switching conditions. In the traditional control case, the active power peaks of VSG1 and VSG2 reach approximately 30 kW and 40 kW, with an oscillation period of about 1.8 s, whereas the proposed strategy suppresses the oscillatory process and enables the output powers to rapidly reach the preset sharing ratio. In addition, the system frequency can recover to the rated value of 50 Hz without obvious steady-state deviation, and the high-frequency component of the grid-connected current and the interphase circulating current are significantly attenuated. MATLAB/Simulink simulation results verify that the proposed dual-layer damping strategy provides better power oscillation suppression, circulating current mitigation, and frequency dynamic performance than the conventional VSG control. Full article

In single-phase PWM rectifiers, due to the inherent time-varying characteristics of the source voltage and current as well as the periodic operation of the converter bridge, the instantaneous input power on the AC side inevitably exhibits a twice-fundamental-frequency pulsation. This phenomenon consequently generates a double-line-frequency (100 Hz) voltage ripple on the DC-link capacitor, which causes an inherent contradiction in conventional voltage outer-loop control between steady-state ripple suppression and dynamic response speed. To address this issue, this paper proposes a control strategy based on an Adaptive Time-Delayed Feedforward Neural Network (Adaptive TD-FNN). The proposed method explicitly introduces the delayed voltage error of half a ripple period into the network state input, thereby achieving time-domain decoupling of the 100 Hz low-frequency disturbance. In addition, a physics-driven training framework is constructed by integrating the rectifier’s discrete difference equation, thereby strengthening the network’s capacity to learn the dynamic characteristics of the system. On this basis, a dynamic adaptive smoothness-weight penalty mechanism is designed to adjust the weighting factor of the current command smoothness constraint in the loss function according to the system operating state. Specifically, the penalty weight is increased under steady-state conditions to suppress command oscillations caused by ripple disturbances, while it is rapidly reduced during load or grid-voltage transients to release the network’s transient optimization capability. Simulation and experimental results show that the proposed Adaptive TD-FNN controller can simultaneously achieve smooth steady-state current command output and fast dynamic voltage regulation without introducing additional complex digital notch-filtering algorithms. Compared with conventional dual-loop control, the proposed strategy reduces the total harmonic distortion (THD) of the grid-side input current from 8.45% to 3.42%, satisfying grid-connected power quality requirements. Meanwhile, under large load transients and grid-voltage disturbance conditions, the DC-link voltage recovery time is about 40 ms, verifying the comprehensive advantages of the proposed method in ripple suppression, dynamic response, and operating-condition adaptability. Full article

Open-cathode Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising technology for increasing the endurance of small Unmanned Aerial Vehicles (UAVs), ground robots, e-bikes, and light electric vehicles. However, their performance under realistic operating conditions is strongly influenced by rapid variations in load, temperature, and ambient pressure, which are often neglected in design-oriented or quasi-steady-state analyses. This study experimentally investigates a 1 kW open-cathode PEMFC system, including its balance of plant and a passive supercapacitor buffer, under a representative UAV flight power profile. Steady-state and dynamic tests were conducted to assess polarization characteristics, thermal behavior, parasitic power consumption, and hydrogen utilization. Results revealed significant thermal inertia and hysteresis effects during load transients, causing voltage deviations from steady-state performance and stabilization times exceeding 90 s. The supercapacitor effectively reduced stack current ramp rates, although some high-frequency oscillations remained. Under flight-representative conditions, the system achieved stable operation with average voltaic efficiency ranging from 55.3% to 60.7% and net efficiency ranging from 50.2% to 54.2%. Auxiliary components had a measurable impact on overall performance: cooling fans accounted for 2–6% of stack power during steady operation and approximately 2.5% of total mission energy, while hydrogen purge losses can significantly reduce vehicle endurance. The findings demonstrate the importance of energy-based performance assessment, including auxiliary loads and purge losses, to obtain realistic estimates of efficiency and endurance in dynamic PEMFC-powered applications. Full article

Aggressive technology scaling has led to a significant increase in manufacturing process variations and transistor aging effects, which critically degrade the performance of radio frequency (RF) circuits. These reliability challenges are particularly pronounced in voltage-controlled oscillators (VCOs), where phase noise and operating frequency stability are compromised. While design strategies incorporating micro-electromechanical systems (MEMS) actuators enhance VCO performance by leveraging MEMS varactors or inductors with substantially higher quality factors (Q), this benefit is progressively undermined over time by process variations and aging-induced shifts in the threshold voltage and carrier mobility of the VCO’s transistors. This work presents an ultra-low-power current-reuse voltage-controlled oscillator (VCO) designed to maintain stable performance under process variability and reliability-induced parameter shifts. Robust operation is achieved using a self-detecting–correcting (SDC) bias scheme that senses performance drift and applies corrective feedback through body-bias control in the VCO core. Analytical relations are derived to describe the impact of threshold voltage and mobility variations, and the approach is validated via post-layout simulations in a 130 nm complementary metal-oxide semiconductor (CMOS). Under 18% variations in threshold voltage and carrier mobility, the proposed SDC scheme preserves oscillation frequency, phase noise, and figure of merit (FoM) while also mitigating the intrinsic output amplitude imbalance of conventional current-reuse VCOs. Monte Carlo analysis (500 runs) demonstrates low sensitivity to fabrication uncertainty, with a standard deviation below 0.14 dBc/Hz for phase noise, 210 kHz for oscillation frequency, and 0.4 dBc/Hz for FoM. The VCO operates from a 0.9 V supply, consumes 175 μW, and achieves −124 dBc/Hz phase noise at 1 MHz offset near 2.4 GHz (FoM ≈ −199 dBc/Hz). Full article

In modern power-system load frequency control (LFC), proportional–integral–derivative (PID) controllers are widely used because of their simple structure and ease of implementation. However, the combined effects of communication delay and nonlinear constraints can degrade control performance. To address this issue, this paper proposes a model-constraint-aware optimal PID tuning method based on a Hybrid Differential Evolution–Chaotic Grey Wolf Optimizer (HDE-CGWO). First, a nonlinear LFC model incorporating data sampling, communication delay, governor deadband (GDB), and generation rate constraint (GRC) is established, and a PID-based LFC model is formulated. Next, an objective function based on the integral of time-weighted absolute area control error (ACE), namely ACE-based integral of time-weighted absolute error (ITAE), is constructed. Accordingly, quasi-opposition-based learning (QOBL), chaotic warm-up, Lévy flight, and differential evolution (DE) are incorporated into the standard Grey Wolf Optimizer (GWO) to develop an HDE-CGWO-based PID design scheme for LFC under sampled-data delay and nonlinear unit constraints. Finally, simulation studies are carried out on a multi-area LFC system. The resulting time-domain responses and statistical results show that, compared with standard GWO in the single-area test, HDE-CGWO reduces the ACE-based ITAE by about 43.3%. In the three-area system, the ACE-based ITAE is reduced by about 3.0% under step disturbances and about 1.4% under random disturbances compared with the warm-up Grey Wolf Optimizer (WGWO), indicating that the proposed method can reduce frequency deviations, attenuate post-disturbance oscillations, and accelerate the dynamic recovery process under the considered disturbance conditions. Full article

Hybrid renewable energy systems (HRES) integrating photovoltaic arrays (PV), wind turbines (WT), and fuel cells (FC) require coordinated maximum power extraction to maintain stable operation under dynamic environmental and load conditions. Conventional MPPT approaches based on independent source-level control often suffer from adverse source interaction, increased steady-state oscillation, degraded DC-link stability, and reduced total extracted power when multiple renewable sources operate simultaneously. To address these limitations, this paper proposes an integrated perturb-and-observe control framework for coordinated power optimization in photovoltaic–wind–fuel-cell hybrid renewable energy systems connected through a shared DC-link structure. Unlike conventional independent MPPT controllers, the proposed strategy evaluates the aggregate power behavior of the integrated system and performs coordinated duty-cycle adaptation to improve renewable-energy utilization while suppressing source conflicts and dynamic coupling effects. The proposed controller is implemented and validated using a real-time digital simulator under a sequential disturbance profile consisting of an irradiance drop at 0.2 s, wind-speed increase at 0.4 s, hydrogen-pressure fluctuation at 0.6 s, and load variation at 0.8 s. Comparative evaluation against conventional perturb-and-observe, incremental conductance, and fuzzy-logic-based MPPT methods demonstrates that the proposed framework achieves a tracking efficiency of $97.8\%$, reduces steady-state tracking error to $2.2\%$, and improves settling time by $42.8\%$ under these dynamic operating conditions. In addition, the proposed controller exhibits lower oscillatory behavior, improved extracted renewable power, and enhanced DC-link stability during simultaneous multi-source disturbances. The results demonstrate that the proposed framework provides an effective real-time coordination strategy for hydrogen-enabled hybrid renewable energy systems operating under dynamically coupled renewable-source conditions. Full article

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic ($u\mid u\mid$) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit ($R^2$) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems. Full article

Maximum power point tracking (MPPT) is a critical function for maximizing energy extraction in photovoltaic (PV) systems. Due to the inherently dynamic nature of the maximum power point under varying irradiance conditions, achieving fast convergence, low steady-state oscillations, and high tracking efficiency remains a challenging research problem. This paper proposes a hybrid ANN-based MPPT strategy for photovoltaic systems operating under rapidly changing environmental conditions. The proposed approach integrates a rule-based operating-condition estimation stage with a recurrent ANN-based control stage, enabling adaptive duty-cycle generation using measured PV voltage and current signals. Unlike conventional MPPT techniques, the proposed method utilizes operating-region estimation together with an extended ANN input feature vector and a recurrent backpropagation neural network to improve dynamic tracking performance under abrupt irradiance variations. In addition, a composite loss function is adopted to enhance tracking accuracy, guidance consistency, and control smoothness. The ANN is initially trained offline and subsequently refined online using lightweight incremental adaptation to maintain effective operation with a low computational burden. The proposed MPPT strategy is evaluated against P&O, FLC, and SMC. Simulation results demonstrate improved tracking performance, faster dynamic response, and reduced steady-state oscillations under abrupt irradiance variations. Full article

The evolution of neutron scattering from reactor-based steady-state sources to high-power pulsed spallation sources has necessitated a paradigm shift in neutron optics. While pulsed sources offer high peak brilliance and energy-resolved measurements via the time-of-flight (TOF) technique, the intrinsic divergence and broad wavelength bandwidth of the incident beam pose significant challenges for focusing, particularly in the realm of very small-angle neutron scattering (VSANS, Q < 0.001 Å⁻¹). This review presents a comprehensive analysis of diverse focusing techniques, including converging multi-slit apertures, electrical and superconducting magnetic sextupole lenses, grazing-incidence focusing mirrors, compound refractive lenses with oscillation apertures, and a special multi-beam VSANS configuration. Special attention is given to the transition from permanent magnet systems to nested rotating sextupole permanent magnets (Nest-Rot-SPM) and modulated superconducting sextupoles (SSM), detailing the physical and engineering challenges involved. Furthermore, grazing-incidence reflective optics, notably toroidal Wolter mirrors, are discussed as an achromatic alternative. The integration of these technologies into world-leading pulsed neutron sources is reviewed to project the future landscape of extended Q-range coverage for SANS instruments. Full article

With the continuous reduction in equivalent inertia in modern power systems, thermal power units are required to provide faster and more sensitive primary frequency regulation. Under this background, small frequency deviation amplification (compensation) has been widely implemented in governing systems. However, while such high-gain control improves frequency response performance, it may significantly deteriorate system damping and even induce low-frequency oscillations (LFO). The underlying mechanism, however, has not been fully clarified from a theoretical perspective. To address this issue, a refined electromechanical coupled model of a thermal power unit governing system incorporating small frequency deviation amplification is established, and the corresponding linearized model is derived. Based on the damping torque analysis method, the influence of amplification gain on mechanical damping is rigorously analyzed in the frequency domain, and the fundamental mechanism leading to damping degradation is revealed. Furthermore, time-domain simulations are conducted to compare system dynamic responses under different compensation parameter settings. The results indicate that the amplification gain has a significant impact on LFO characteristics. Improper parameter settings can directly reduce system damping and trigger oscillations. Full article

We investigatewhether a finite local information capacity of spacetime can account for the gravitational phenomena commonly attributed to cold dark matter. Starting from a covariant effective-field-theory description, we modelcoarse-grained entropy deposition as a dynamical scalar field $S\left(x\right)$ whose stress–energy tensor contributes to structure formation. The macroscopic action contains a single dimensionless coupling λ multiplying the canonical kinetic term, ensuring ghost-free dynamics and conservation of the associated stress–energy tensor. In a slow-roll regime, defined by a covariant source term $\Gamma \equiv \ddot{S}+3H\dot{S}=0$, where H is the Hubble parameter and overdot denotes derivative with respect to cosmic time, and $|\ddot{S}|\ll H|\dot{S}|$, the entropy sector behaves as pressureless dust at background and in linear order. Implemented in a modified Cosmic Linear Anisotropy Solving System (CLASS) Boltzmann solver, the entropy component fits Planck satellite 2018 cosmic microwave background (CMB) data, baryon acoustic oscillation (BAO) measurements, and the Pantheon + Type Ia supernova sample for $0.5\lesssim \lambda \lesssim 2$, while preserving the linear growth factor to within 0.2% over Euclid space telescope scales. To regulate ultraviolet contributions, we introduce a holographically motivated prescription in which gravitationally active entropy deposition is confined to causal two-surfaces, yielding a $\rho \propto r^{-2}$ halo envelope with a finite-density core determined by local entropy saturation. Fixing the flux scale $\mathcal{A}$ from astrophysical entropy budgets reproduces Milky-Way-mass halos without introducing fine-tuned length scales. Pilot N-body simulations that evolve the entropy field on a staggered grid reproduce the halo mass function down to ${10}^{10.5}{M}_{\odot }$, mitigate the cusp–core and missing-satellite tensions, and remain consistent with cluster lensing constraints. On linear scales, the model predicts percent-level, scale-dependent deviations in the lensing convergence and matter power spectra, testable by Euclid space telescope, the Roman Space Telescope High Latitude Survey, and the CMB-S4 experiment. Full article

High-power 355 nm ultraviolet (UV) lasers, leveraging their short wavelength, high photon energy, and high absorption across a broad range of materials, have become indispensable light sources for precision manufacturing, semiconductor processing, and laser direct imaging (LDI). In this paper, we demonstrate a high-power 355 nm UV laser system based on a narrow-linewidth polarization-maintaining (PM) Yb-doped fiber laser and cascaded frequency conversion. A single-frequency semiconductor laser is employed as the seed source, with its spectral linewidth broadened to 0.32 nm (full width at half maximum, FWHM) via phase modulation to suppress stimulated Brillouin scattering (SBS). Through a PM master oscillator power amplifier (MOPA) architecture, a maximum average output power of 899 W at 1064 nm is achieved with a beam quality factor of M² = 1.12 (M²_x = 1.11, M²_y = 1.13). By employing lithium triborate (LiB₃O₅, LBO) crystals for extracavity cascaded second-harmonic generation (SHG) and sum-frequency generation (SFG), a maximum green output power of 613.7 W at 532 nm is obtained, corresponding to a SHG conversion efficiency of 68.2%, and a maximum UV output power of 227.1 W at 355 nm is achieved, with a total conversion efficiency of 25.2%. At the maximum output power, the UV beam quality factors are M² = 1.16 (M²_x = 1.24 and M²_y = 1.09), and the power fluctuation is better than ±1.5% root-mean-square (RMS) over 8 h of continuous operation. These results indicate that the cascaded frequency conversion approach based on narrow-linewidth PM fiber lasers possesses the capability for further scaling to higher-power single-path high-brightness UV output and can provide high-brightness UV sources for applications such as flexible printed circuit (FPC) laser cutting, flat-panel display laser direct imaging, and semiconductor wafer scribing. Full article

Power electronic DC–DC conversion stages play a pivotal role in photovoltaic (PV) energy conversion. Here, maximum power point tracking (MPPT) is necessary to regulate the operating point of the converter with high bandwidth and robustness in the presence of irradiance and temperature disturbances. This paper proposes an MPPT scheme based on an Intelligent Wave Algorithm (IWA) for a PV source connected to a flyback DC–DC converter. The proposed IWA is formulated as a population-based metaheuristic that updates the converter’s duty cycle to maximize PV power while reducing the oscillations commonly observed in classical methods. A unified MATLAB/Simulink test bench has been developed in which multiple MPPT algorithms—Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO) and the proposed IWA—are implemented in parallel flyback subsystems that share the same PV module and converter parameters. The simulation results show that the IWA method achieved consistent convergence to the maximum power point more rapidly than both classical and advanced meta-heuristic methods, obtaining 12.5% better response time and 8.9% better steady-state output power than the method closest to it. Overall, the findings suggest that combining a flyback converter with IWA-based maximum power point tracking (MPPT) improves the efficiency and stability of energy harvesting, making this approach suitable for low- to medium-power photovoltaic (PV) applications within modern power electronics conversion systems. Full article

With high renewable penetration, power system oscillations become more complex. Since internal control details of renewable stations are often inaccessible, classic participation analysis relying on detailed models is difficult to apply, making weak node identification urgently needed. To address this problem, this paper proposes a residue-centered impedance-based method for small-signal stability in renewable energy-dominated power systems. First, an equivalent state-space model is built from station impedance models, linking the black-box impedance and white-box state-space participation analysis. Then, the physical essence of weak node identification is analyzed, and a residue-centered participation factor is introduced as the indicator. Subsequently, the effect of the station impedances at weak nodes on system stability is quantified. Finally, the method is validated on a four-station testing system and a real-life renewable energy-dominated power system. The rank correlation between the proposed method and the traditional state-space method is close to 1, demonstrating its effectiveness for system-level weak node identification. The proposed method provides engineering guidance for parameter tuning and damping control in practical power systems, which can help improve renewable energy accommodation and support low-carbon, secure, and sustainable power system operation. Full article

This study investigates the coordinated impact of a synchronous condenser (SC), battery energy storage system (BESS), and static synchronous compensator (STATCOM) on enhancing voltage and frequency stability in a modified IEEE 9-bus power system under severe disturbances. The aim is to quantify the individual and combined contributions of these technologies during both fault ride-through (FRT) and load-increment events. The methodology includes dynamic modelling of all three devices in DIgSILENT PowerFactory. The SC is represented as a synchronous machine with inertia and AVR-based voltage control; the BESS employs converter-based active power and frequency-droop control; and the STATCOM provides fast reactive power injection through a dual-loop voltage regulator. Key indicators include nadir (minimum frequency), Rate of Change of Frequency (RoCoF), steady-state deviation, voltage sag depth, and recovery characteristics. Results indicate distinct roles for each device. The SC increases inertia and improves damping, but it also introduces small, well-damped oscillations. The BESS significantly enhances frequency stability by mitigating nadir, reducing RoCoF, and accelerating recovery, with negligible effect on voltage regulation. The STATCOM substantially reduces voltage sag and speeds up voltage recovery, but it does not influence frequency behaviour. When combined, the hybrid SC–BESS–STATCOM system demonstrates strong complementarity: the SC supports inertia, the BESS stabilizes active-power imbalance, and the STATCOM ensures fast reactive-power compensation. Full article