Search Results (719)

The increasing deployment of Renewable Energy Sources (RESs), particularly wind and solar power, plays a critical role in reducing carbon emissions and supporting sustainable energy transitions. However, the large-scale integration of RESs into smart grids introduces significant technical challenges related to frequency stability, voltage regulation, rotor angle stability, power quality, inertia reduction, harmonic distortion, reverse power flow, Sub-Synchronous Interactions (SSIs), and protection coordination. Although numerous review studies have examined renewable energy integration, most focus on high-level frameworks, bibliometric analyses, optimization techniques, or isolated applications of artificial intelligence (AI) while lacking a comprehensive synthesis that bridges AI-driven solutions with the physical dynamics, control mechanisms, and protection requirements of highly renewable power systems. To address this gap, this review provides a comprehensive technical assessment of wind generator topologies, solar inverter architectures, grid-forming and grid-following control strategies, virtual inertia and virtual Synchronous Generator (SG) technologies, adaptive load-frequency control, energy storage integration, protection coordination, and real-time stability enhancement techniques for high-RES smart grids. Furthermore, the review systematically examines the role of AI in frequency regulation, voltage control, harmonic mitigation, predictive operation, parameter optimization, and system resilience. Unlike previous reviews, this study integrates physical-layer perspectives by connecting AI-driven decision-making with practical grid control mechanisms, inverter dynamics, wide-area monitoring, microgrid operation, High Voltage Direct Current (HVDC) interconnections, EV/Vehicle-to-Grid (V2G) integration, and multi-resource energy management. The review identifies key research priorities, including the development of real-time AI-assisted frequency control, adaptive protection schemes for low-inertia systems, coordinated grid-forming inverter control, resilient autonomous grid operation, and scalable multi-energy management frameworks. The findings provide actionable guidance for researchers, utilities, policymakers, and industry stakeholders seeking to enhance stability, reliability, and operational flexibility in future smart grids with very highly renewable energy penetration. Full article

Compared with conventional air separation columns, the internally heat-integrated air separation column (HIASC) offers superior energy efficiency. However, its structural complexity poses significant challenges for model-based online optimization and control. This study proposes a nonlinear wave model based on a model updating strategy, which substantially reduces modeling complexity. First, wave propagation theory is employed to characterize the concentration distribution profiles and their propagation velocities within the HIASC. Subsequently, a localized analytical method based on the distributed wave velocity is developed from local mechanistic insights to evaluate waveform distortion. Furthermore, a model updating strategy is introduced, which determines the optimal updating frequency according to the degree of waveform deformation, thereby mitigating computational redundancy caused by excessive updates. Finally, the proposed strategy is integrated into the nonlinear wave model to achieve an optimal balance between accuracy and computational efficiency. Simulation results validate the effectiveness and robustness of the proposed modeling approach. Full article

To investigate the transient muzzle arc and its influence on the post-exit dynamics of the armature in electromagnetic rail launch (EMRL) systems, a bidirectionally coupled multiphysics simulation framework integrating ANSYS Maxwell and Fluent is developed. A 10 × 10 mm scaled-caliber launcher with a 1.9 g aluminum-alloy armature is considered to quantitatively characterize the coupled evolution of the electromagnetic field, temperature field, and arc-induced flow field after armature–rail separation. The results show that the air-gap breakdown arc reaches a peak temperature of approximately 35,873 K at the early post-exit stage, producing concentrated thermal loads near the armature tail and rail leading edges. The rapid spatial expansion of the arc further redistributes the current density and distorts the local electromagnetic field, resulting in asymmetric Lorentz forces acting on the unconstrained armature and pronounced transient acceleration disturbances along multiple axes. Comparative simulations with and without a bypass arc chute demonstrate that the additional conduction path promotes rapid current commutation and suppresses the spatial expansion and persistence of the muzzle arc. Consequently, the arc-induced electromagnetic disturbance is substantially reduced, and the armature acceleration responses converge toward zero within approximately 50 μs after muzzle exit. These results establish a quantitative link between transient arc evolution, electromagnetic-field distortion, and post-exit armature dynamic disturbance, and demonstrate the potential of bypass arc chutes for simultaneously mitigating muzzle thermal loading and improving post-exit flight stability in EMRL systems. Full article

The increasing penetration of nonlinear industrial loads, distributed renewable generation, and intelligent electrical infrastructures requires active power filters capable of simultaneously providing high-performance harmonic mitigation, reactive power compensation, coordinated operation of multiple converters, and deterministic real-time implementation. Conventional centralized shunt active power filters (SAPFs) exhibit limited scalability, while optimization-based approaches often compromise deterministic execution because of their computational complexity. To address these challenges, this paper proposes a Distributed Quantum Multi-Shunt Active Power Filter (Quantum Multi-SAPF) that combines deterministic-based local current control with asynchronous quantum-assisted supervisory optimization. The proposed architecture employs four distributed SAPF units operating under a hierarchical cyber–physical framework. The lower control layer, implemented on a MATLAB R2026a, includes all fast electrical functions—signal acquisition, SOGI-based synchronization, Clarke transformation, instantaneous $p$–$q$ current reference generation, current regulation, interleaved PWM modulation, and protection—which are executed deterministically at a switching frequency of 15 kHz. The upper supervisory layer operates asynchronously at 20 Hz and formulates converter coordination as a quadratic unconstrained binary optimization (QUBO) problem solved using Quantum Approximate Optimization Algorithm (QAOA) allocation together with Variational Quantum Eigensolver (VQE) predictive correction. This multi-rate architecture separates fast electrical dynamics from slow supervisory optimization, ensuring that uncertain optimization latency does not affect converter stability. The proposed controller is validated through comprehensive switching-level simulations on the MATLAB R2026a platform. Numerical results demonstrate a reduction in source current total harmonic distortion from 24.615% to 0.142%, corresponding to a 99.423% harmonic reduction, while improving the source power factor to 0.99999 and achieving 99.999% reactive power compensation. The distributed four-SAPF synchronization network maintains coherent phase alignment among all converter units throughout the simulation, thereby supporting coordinated compensation and balanced current sharing. This synchronized operation contributes to highly accurate compensation current tracking, with an RMS tracking error of only 0.026 A, while limiting the source current unbalance to 0.026%. These results confirm the effectiveness of the distributed synchronization and local control architecture in maintaining coordinated and balanced operation of the four parallel SAPFs. The proposed interleaved modulation strategy, combined with optimized current sharing, maintains balanced converter utilization while suppressing circulating currents without requiring a dedicated circulating current controller. The proposed Distributed Quantum Multi-SAPF establishes a scalable framework that combines deterministic industrial control with quantum-assisted supervisory optimization. The architecture provides high harmonic compensation capability, near-unity power factor, balanced converter utilization, comprehensive Safe Operating Area supervision, and practical industrial feasibility, making it a promising solution for future smart grids, renewable energy integration, electric vehicle charging infrastructures, and intelligent power quality conditioning systems. Full article

To clarify the effects of rotor-induced downwash and crosswind on liquid sheet breakup and spray atomization characteristics of agricultural UAVs, an experimental platform integrating particle image velocimetry (PIV), a UAV spray system, and a wind tunnel was established. The droplet size and velocity characteristics of a flat-fan nozzle were investigated under different rotor speeds, crosswind conditions and spray pressures. The results showed that rotor-induced airflow significantly altered the post-breakup droplet characteristics. As the rotor speed increased from 0 to 2200 rpm, the volume median diameter (D_V0.5) increased from 206.45 to 245.06 μm (18.7%), while the volume fraction of droplets smaller than 150 μm (V_<150 (%_vol)) decreased from 12.86% to 10.26%, indicating a shift toward coarser droplets under stronger downwash. Crosswind exhibited a limited influence on the primary breakup process but substantially modified droplet transport. Without rotor operation, increasing crosswind velocity from 0 to 6 m/s reduced the mean horizontal droplet velocity by 77.0%, promoting lateral droplet displacement. Under rotor operation at 2000 rpm, the downwash effectively enhanced spray plume stability and mitigated crosswind-induced distortion. Furthermore, increasing spray pressure from 0.10 to 0.50 MPa reduced D_V0.5 from 274.29 to 222.24 μm and increased the proportion of fine droplets, demonstrating that spray pressure was the dominant factor controlling primary atomization. Overall, rotor-induced airflow primarily regulated droplet redistribution after atomization, whereas crosswind mainly affected droplet transport behavior. These findings provide theoretical guidance for optimizing UAV spray parameters and improving precision pesticide application efficiency. Full article

With the growing penetration of renewable energy, accurately characterizing the frequency support performance of grid-following wind and photovoltaic (PV) units has become increasingly important. However, conventional methods for assessing frequency support parameters often overlook practical dynamic effects, making it difficult to determine the support parameters actually realized during disturbances. To address the time-domain coupling, differential noise amplification, and parameter distortion problems in the online identification of virtual primary frequency regulation and virtual inertia coefficients, this paper establishes a frequency response model for wind and PV units that incorporates these support mechanisms together with practical physical constraints. On this basis, a two-stage forgetting-factor recursive least squares (FF-RLS) method is proposed to identify realized frequency support parameters. Exploiting the difference in response time scales between primary frequency regulation and inertial support, a quasi-steady-state frequency regulation window and a transient inertia window are constructed to decouple the two parameters. Meanwhile, Tustin phase compensation and band-limited differentiation are introduced to mitigate measurement noise and the phase mismatch between frequency and power responses. Finally, a stable window criterion is developed to adaptively extract reliable identification intervals. Simulation studies on a modified IEEE 24 bus system, together with comparisons against conventional identification methods, demonstrate the effectiveness and accuracy of the proposed method. Full article

In practical communication systems, signals suffer from attenuation during transmission over channels, particularly in multipath environments where inter-symbol interference (ISI), fading, and noise further degrade signal quality. To mitigate channel-induced distortion, receivers typically employ equalization techniques. Common channel equalization methods include linear equalizers, adaptive equalizers, and decision feedback equalizers (DFEs). Single-carrier frequency-domain equalization (SC-FDE) systems can effectively combat frequency-selective interference. Compared with time-domain equalization, frequency-domain equalization not only improves compensation performance but also significantly reduces computational complexity. This paper proposes a correlation-aware multi-observation SC-FDE receiver for frequency-selective multipath channels. The proposed receiver employs sliding windows to obtain multiple equalized observations of the same target symbol, thereby exploiting the observation diversity associated with different symbol positions and channel conditions. These observations are subsequently combined using an MVDR-based combiner, which suppresses position-dependent residual ISI while preserving the desired signal under a distortionless constraint. Through simulation tests, the proposed MVDR-based sliding-window scheme achieves favorable equalization performance under various channel models. Full article

Background/Objectives: Fictional portrayals of criminal investigation have long inflated public expectations of forensic science, in a phenomenon known as the CSI effect. As artificial intelligence, data science and three-dimensional reconstruction enter operational policing, that distortion extends into what we term the Digital CSI effect. This study examines how narrative-driven, interactive science communication can recalibrate such expectations while building technological and ethical literacy among adult audiences. Methods: Using a descriptive multiple-case design, we examined four deliveries of two workshops involving 98 adult participants across three audience types: general public, law enforcement practitioners and trainees, and postgraduate students. Workshop 1 addressed web scraping and data visualisation in homicide investigation, together with their legal and ethical limits. Workshop 2 introduced artificial intelligence and 3D crime scene reconstruction in an immersive virtual environment. Four propositions were formulated before delivery and examined at each case against facilitator observation, question-and-answer sessions and open-ended written feedback. Results: Participants reported increased familiarity with the demonstrated technologies and more realistic expectations of what those technologies can deliver. Unrealistic assumptions about the speed and clarity of evidence interpretation surfaced repeatedly, particularly among audiences without investigative experience, and were addressed directly during the sessions. Participants valued tailored content, hands-on interaction and narrative framing, and identified orientation in the virtual environment as the main practical barrier. Conclusions: Narrative-centred, interactive workshops offer a practical and transferable mechanism for mitigating the Digital CSI effect and for building ethical awareness among adult learners in and around forensic practice. Full article

Achieving a balance between high energy-storage density and high charge/discharge efficiency remains a key challenge for polymer film capacitors. In this study, a gradient-dielectric all-organic sandwich composite film (PFHP) was fabricated, comprising a polypropylene (PP) outer layer, an irradiation-cured in situ acrylate (HD) intermediate layer, and a polyvinylidene fluoride (PVDF) outer layer. The HD layer, with its moderate dielectric constant (ε_r ≈ 4.2), mitigates the dielectric discontinuity and local electric field distortion at the PP/PVDF interface. Furthermore, in situ curing forms a dense interface and deep traps, thereby suppressing carrier migration and conduction losses. This structure synergistically combines the high polarization capacity of PVDF with the high breakdown strength and low loss characteristics of PP. Under a maximum test electric field of 450 MV m⁻¹, the PFHP film achieved a discharge energy storage density of 4.09 J cm⁻³, with a charge–discharge efficiency of over 90%. This energy storage density was 2.13 times that of the pure PP film under the same electric field. Overall, the introduction of an acrylate interlayer not only mitigates the dielectric constant mismatch at the interface but also regulates charge transport by forming a cross-linked network, thereby suppressing leakage current and space charge accumulation, which helps improve energy storage performance. This work provides an effective strategy for developing all-organic thin-film capacitors with high energy storage capacity and high efficiency. Full article

This paper presents an adaptive outer-loop neural PID control scheme with asymmetric saturation and clamping anti-windup for a quadcopter drone, using online training based on the Extended Kalman Filter. This proposal mitigates the effects of actuator saturation and maintains operational feasibility under physical constraints. Multirotors possess distinct aerodynamic capacities, requiring continuous thrust for vertical gravity compensation versus tilt-induced forces for horizontal translation. Therefore, the proposed framework incorporates coupled asymmetric saturation limits to prevent directional vector distortion alongside a clamping-based anti-windup mechanism and dynamic tuning for controller gains. Full article

To address the performance degradation of target elevation estimation for deep-sea vector hydrophone vertical arrays under strong interference, this paper proposes an inverse beamforming (IBF)-based interference suppression method for such vertical arrays. The proposed method exploits the array spatial response characteristics to detect, reconstruct, and iteratively cancel dominant interference components. An adaptive stopping strategy is adopted to prevent excessive cancellation and reduce the risk of target self-cancellation. Furthermore, it utilizes the coherence properties between multiple physical channels to perform joint processing of sound pressure and particle velocity channels, thereby enhancing the target-direction response and mitigating residual interference. To alleviate the performance degradation induced by array element failures, this paper introduces a least-squares-based array output reconstruction method to restore the spatial sampling structure and reduce array-manifold distortion. Simulation and sea trial results demonstrate that, compared with conventional beamforming (CBF) and minimum variance distortionless response (MVDR), the proposed method effectively suppresses strong directional interference and recovers the target-direction spatial response. The median spatial-spectrum contrast between the target region and the interference region improves from −1.21 dB before IBF to 2.70 dB after IBF, with a median improvement of 4.12 dB over the full observation interval. The mean estimated elevation angle is 46.5°, close to the reference value of 47.2°, with an RMSE of 0.76° and a success rate of 100% within ±2° and ±3° tolerances. These results indicate that the proposed method enables reliable target elevation estimation under strong interference and remains effective in the presence of array element failures, demonstrating its suitability for practical underwater applications. Full article

To address the issues of subcarrier orthogonality loss and inter-carrier interference (ICI) caused by carrier frequency offset (CFO), this paper proposes and experimentally validates a frequency offset estimation (FOE)-assisted dual-domain Transformer equalizer within an advanced, high-capacity optical-heterodyne radio-over-fiber (RoF)–wireless orthogonal frequency division multiplexing (OFDM) transmission system. To rigorously test the algorithm’s robustness under extreme physical conditions, the experimental platform integrates offline 16-GBaud signal generation, optical I/Q modulation, dual-optical-tone transport over a single-mode-fiber RoF feeder, remote photonic heterodyne frequency conversion based on a uni-traveling-carrier photodiode (UTC-PD), 4.6 km free-space wireless transmission, and 160-GSa/s ultra-high-speed real-time sampling. In this system, the receiver front-end employs an FOE module to pre-compensate for the dominant global CFO-induced phase rotation; subsequently, a low-complexity, compact local-window Transformer is utilized to perform adaptive residual compensation for local data-dependent impairments—such as residual waveform distortion and residual ICI—in both the time and frequency domains (before and after the Fast Fourier Transform, or FFT). This synergistic architecture, combining a physical model-driven approach with a self-attention mechanism, effectively mitigates the adverse impact of global frequency offset on neural network convergence. Experimental results demonstrate that, under conditions of strictly aligned multiply accumulate (MAC) operation complexity, the dual-domain architecture achieves significantly superior performance—in terms of bit error rate (BER), error vector magnitude (EVM), and constellation quality—compared to traditional linear DSP methods and baseline networks such as DNNs, CNNs, and LSTMs. Operating in 16 GBaud QPSK mode with an input optical power of 0 dBm, the system achieves a BER of $1.89\times {10}^{-4}$, representing performance improvements of approximately 5.98-fold and 1.92-fold over the standalone Transformer and FOE-assisted DNN schemes, respectively. Full article

Small cargo unmanned aerial vehicles (UAVs) operating in narrow confined spaces suffer instantaneous collision impacts that induce gyroscope heteroscedastic noise and abrupt angular velocity derivatives, triggering severe dynamic attitude errors in traditional strapdown inertial navigation systems (SINS). Existing algorithms separate angular velocity fitting and noise suppression, adopt unified three-axis weighting, and lack adaptive segmentation for collision disturbances, limiting navigation accuracy without raising computational costs. This paper proposes an integrated heteroscedastic decoupling algorithm for UAV SINS under collision interference. Hermite orthogonal polynomials are utilized to fit non-stationary angular velocity with derivative matching constraints, an optimized single-pass CUSUM detector with steady-state residual compensation is proposed to identify collision-induced variance change points. An axis-differentiated weighting strategy is developed to suppress heteroscedastic noise. Recursive least-squares is adopted to lower online computation overhead. Multi-condition coning motion simulations show Hermite polynomials achieve the lowest attitude RMSE under steady flight; the improved CUSUM detector delivers shorter detection delay, fewer false alarms, and lighter computation than mainstream detection methods, and segmented differentiated weighting eliminates collision-induced noise distortion at the raw measurement stage. The proposed algorithm unifies signal fitting and noise correction with minimal computational overhead, effectively mitigating dynamic errors for lightweight airborne navigation hardware and offering a high-precision front-end preprocessing solution for cargo UAVs operating in cluttered obstacle environments. The proposed algorithm is positioned as a gyro-only front-end preprocessing module; accelerometer-related error compensation and full multi-sensor back-end integration are addressed in ongoing work. Full article

Distributed multiple-input multiple-output (MIMO) is a promising architecture for future wireless systems because cooperation among geographically separated base stations (BSs) improves coverage, spectral efficiency, and link reliability. However, the large effective aperture formed by distributed BSs makes near-field effects non-negligible and complicates accurate channel state information acquisition. Existing near-field estimators often suffer from modeling errors caused by approximate angle–range decoupling or from the high storage and computational costs of dense two-dimensional sparse representations. This article proposes an off-grid variational Bayesian channel-estimation framework for the considered distributed near-field MIMO geometry, which comprises equally spaced, collinear BS reference points and aligned uniform linear arrays (ULAs) with common inter-element spacing. We establish a geometry-coupled model based on the exact geometric spherical-wave phase response and map the local direction–range parameters observed by different BSs into a common reference coordinate system, yielding a two-dimensional jointly sparse representation. An independent-vector variational Bayesian inference algorithm then decomposes the high-dimensional multiuser recovery problem into user-specific posterior subproblems. It operates directly on the received pilot matrices, avoiding pilot–matrix inversion and the resulting distortion of noise statistics. A two-dimensional skewed off-grid update is further embedded in an expectation-maximization procedure to jointly refine angle and range offsets, mitigating basis mismatch while permitting a coarser initial dictionary. Simulation results support the effectiveness of the proposed method in the evaluated scenarios. Full article

Objective: This research examined the effectiveness of liposomal N-Curcumin (N-Cur) against Cis-diamminedichloroplatinum (CDDP)-induced hepatotoxicity, specifically examining its ability to influence the Wnt/β-catenin/GSK-3 pathway and associated molecular markers. Methods: Male Wistar rats were treated for 14 days with oral N-Cur (80 mg/kg) and with a single intraperitoneal dose of CDDP (7 mg/kg) administered on day 7. Hepatic integrity was assessed through liver injury markers, histopathological examination, and biochemical analysis of oxidative stress (SOD, GSH, and MDA), inflammation (IL-10, TNF-α, CRP, and NF-κB p65), and signaling protein expression (β-catenin, Nrf2, and GSK-3). Results: CDDP administration resulted in significant hepatic damage, characterized by elevated injury markers and distorted tissue architecture. It induced severe oxidative stress (increased MDA; decreased GSH and SOD) and a robust inflammatory response. At the molecular level, CDDP suppressed the cytoprotective β-catenin and Nrf2 pathways while increasing GSK-3. Conversely, N-Cur treatment effectively reversed these pathological shifts by restoring antioxidant defenses, inhibiting pro-inflammatory mediators, and normalizing the Wnt/β-catenin/GSK-3 signaling axis. Conclusions: Liposomal N-Cur demonstrates significant potential as a hepatoprotective agent when administered concomitantly with CDDP chemotherapy. N-Cur mitigates oxidative damage and inflammation, thereby preserving hepatic function during CDDP-based chemotherapy. In addition, these favorable effects were associated with modulation of the Wnt/β-catenin/GSK-3 and Nrf2 pathways. Full article