Search Results (8,302)

Photovoltaic (PV) systems are vital to contemporary renewable energy generation systems. However, complex operating conditions, such as variable loads, grid uncertainty, and unstable sunlight, pose a serious threat to the stability of the power system integrated with PV generation. To maintain stable operation under such conditions, PV systems must dynamically regulate their power output through a boost converter, thereby preventing excessive DC bus voltage and power levels. This article first summarizes practical control requirements for PV systems under complex operating conditions and subsequently proposes a multi-objective control method for boost converters in PV applications to enhance system adaptability. The proposed strategy enables seamless transitions between operating modes, including DC-link voltage control, current control, power control, and maximum power point tracking (MPPT). The dynamic behavior of the control method during mode switching is theoretically analyzed. Simulation results verify the correctness of the analysis and demonstrate the effectiveness of the proposed method under challenging PV operating conditions. Full article

High-penetration integration of distributed photovoltaics (PV) into distribution networks introduces significant challenges regarding voltage limit violations and fluctuations. To address these issues, this manuscript proposes a hierarchical coordinated voltage control strategy for medium- and low-voltage distribution networks utilizing a cloud-edge collaboration architecture. The research methodology involves constructing a multi-objective optimization model at the cloud layer to minimize network losses and voltage deviations, solved via an improved Honey Badger Algorithm (HBA). Simultaneously, at the edge layer, a multi-mode coordinated control strategy incorporating Virtual Synchronous Generator (VSG) technology is developed to provide fast reactive power support and inertial response. Through simulation analysis on an IEEE 33-node test system, the findings demonstrate that the proposed strategy significantly mitigates voltage fluctuations and enhances the hosting capacity of distributed energy resources. The study concludes that the cloud-edge framework effectively decouples control time-scales, ensuring both global economic operation and local transient stability. These results are significant for advancing the resilient operation of active distribution networks with high renewable penetration. Full article

We report a dispersion-controlled Yb-doped fiber chirped pulse amplification (CPA) system incorporating a tunable chirped fiber Bragg grating (CFBG) stretcher and a single-grating transmission compressor for dynamic compensation of power-dependent nonlinear effect. During the pulse amplification, the CFBG introduces adjustable third-order dispersion (TOD). By tuning the initial TOD provided by CFBG from −0.1965 ps³ at 2.37 W to −0.1791 ps³ at 9.65 W, residual TOD is efficiently compensated with the power-dependent nonlinear effect. As a result, by optimizing the dispersion balance at each output power, nearly constant femtosecond pulses with a duration of 250 fs are obtained over the entire power range, confirming effective control of nonlinear and dispersive effects in the amplification. The high-quality 1030 nm pulses enable efficient second-harmonic generation (SHG) in a type-I BBO crystal, producing 3.56 W femtosecond output at around 515 nm with a pulse duration of 190 fs, close to the Fourier transform limit. These results demonstrate a robust approach to generating high-power and temporal coherent ultrafast pulses suitable for precision micromachining and two-photon polymerization. Full article

To improve the dynamic response and steady-state frequency quality of a wind–storage coordinated system during primary frequency regulation, and to address the secondary frequency dip caused by rotor kinetic energy recovery when a doubly fed induction generator (DFIG)-based wind turbine (DFIG-WT) participates in frequency support, this paper proposes a coordinated wind–storage primary frequency regulation strategy. This strategy synergistically controls the wind turbine’s rotor kinetic energy recovery and exploits the advantages of hybrid energy storage system (HESS). During the DFIG-WT control stage, an adaptive weighted model is developed for the inertial and droop power contributions of the DFIG-WT based on the available rotor kinetic energy, enabling a rational distribution of primary frequency regulation power. In the control segment of HESS, an adaptive complementary filtering frequency division strategy is proposed. This approach integrates an adaptive adjustment method based on state of charge (SOC) to control both the battery energy storage system (BESS) and supercapacitor (SC). Additionally, the BESS assists in completing the rotor kinetic energy recovery process. Through simulation experiments, the results demonstrate that under operating conditions of 9 m/s wind speed and a 30 MW step disturbance, the proposed adaptive weight integrated inertia control elevates the frequency nadir to 49.84 Hz and reduces the secondary frequency dip to 0.0035 Hz. Under the control strategy where wind and storage coordinated participate in frequency regulation and BESS assist in rotor kinetic energy recovery, secondary frequency dips were eliminated, with steady-state frequency rising to 49.941 Hz. The applicability of this strategy was further validated under higher wind speeds and larger disturbance conditions. Full article

This work proposes a generic model for clarifying the mechanism hidden in the phenomena of fluctuation propagation in grid-forming (GF-VSG) systems, considering the impact of different disturbances. Additionally, a new judgment criterion is established to give physical insights into the power oscillation stability of the GFMC system. And this judgment criterion, as well as the model, can identify the power stability combined with fluctuation propagation phenomenon no matter what the disturbance is, which can also give guidance to the controller design to guarantee that the GFMC can operate in normal operation conditions while leisurely confronting various disturbances. In addition, it is found that the established conventional single closed-loop system may lose effectiveness in judging stability, especially when the oscillation propagation of disturbance occurs. Finally, the proposed model and judgment criteria are demonstrated by experiments. Full article

This study examined the effects of zinc–iron (Zn–Fe) biofortified alfalfa on mineral deposition, growth performance, feed efficiency, and selected meat-quality traits in guinea pigs (Cavia porcellus). Four alfalfa cultivars (Cuf101, Moapa69, California55, and Yaragua) were cultivated under two fertilization levels (0–0 and 2–2 kg ha⁻¹ Zn–Fe). Biofortification increased forage Zn concentrations from 26.8 to 36.4 mg kg⁻¹ to as high as 325.8 mg kg⁻¹, and Fe concentrations from 139.7 to 425.0 mg kg⁻¹ to 450.1 mg kg⁻¹. A total of 48 weaned guinea pigs (initial body weight 0.30 ± 0.01 kg) were allocated to a randomized multi-factorial feeding trial. Growth performance, feed intake, feed conversion ratio (FCR), and tissue mineral concentrations were evaluated over a 35–50 day period and analyzed using a multi-factorial ANOVA within a General Linear Model framework. Dietary biofortification resulted in a significant improvement in feed efficiency, with FCR decreasing from 6.3 in the control diet to 5.8 in the enriched diet, and the lowest FCR was observed in animals fed the California55 cultivar (5.1). No statistically significant sex effect was detected for live weight gain, although males showed higher total weight gain (248.7 g) than females (187.8 g). Tissue Zn (≈20.7 mg kg⁻¹) and Fe (≈10.2 mg kg⁻¹) concentrations in meat were only marginally affected by diet, suggesting strong physiological regulation of mineral deposition. Multivariate analyses indicated that the enriched diet produced more homogeneous meat-quality profiles and reduced inter-animal variability. Overall, Zn–Fe biofortified alfalfa improved feed efficiency without compromising growth performance or meat quality, indicating potential relevance for smallholder guinea pig production systems. However, given the limited sample size per factorial cell, the findings should be interpreted with caution and considered exploratory, warranting confirmation in larger, adequately powered studies. Full article

In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of quantum control methodologies-grounded in established principles of quantum mechanics—into the automation of thermal processes in power plant operations. Specifically, it investigates a hybrid quantum-fuzzy control system for managing steam heating processes, a critical subsystem in thermal power generation. Unlike conventional control strategies that often struggle with nonlinearity, time delays, and parameter uncertainty, the proposed method incorporates quantum-inspired optimization algorithms to enhance adaptability and robustness. The quantum component, based on recognized models of coherent control and quantum interference, is utilized to refine the inference mechanisms within the fuzzy logic framework, allowing more precise handling of state transitions in multivariable environments. A simulation model was constructed using validated physical parameters of a pilot-scale steam heating unit, and the methodology was tested against baseline scenarios with conventional proportional-integral-derivative (PID) control. Experimental protocols and statistical analysis confirmed measurable improvements: up to 25% reduction in fuel usage under specific operational conditions, with an average of 1 to 2% improvement in energy efficiency. The results suggest that quantum-enhanced intelligent control offers a feasible pathway for bridging the gap between quantum theoretical models and macroscopic thermal systems, contributing to the development of more energy-resilient industrial automation solutions. Full article

This paper addresses the limitations of conventional single-stage direct-control maximum power point tracking (MPPT) methods, such as the Perturb and Observe (P&O) algorithm. Fixed-step-size duty-cycle perturbations cause a trade-off between slow tracking with small oscillations and fast tracking with large oscillations, along with poor responsiveness to rapid weather variations and output voltage fluctuations. Two main contributions are presented. First, a fractional-order DC–DC boost converter (FOBC) is introduced, incorporating fractional-order dynamics to enhance system performance beyond improvements in control algorithms alone. Second, a novel indirect-control MPPT strategy based on a two-stage architecture is developed, where the P&O algorithm generates the optimal voltage reference and a fractional-order linear-quadratic-integral (FOLQI) controller—designed using a fractional-order small-signal model—regulates the PV module voltage to generate the FOBC duty cycle. Hardware-in-the-loop simulations confirm substantial performance improvements. The proposed FOLQI-based indirect-control approach with FOBC achieves a maximum MPPT efficiency of 99.26%. An alternative indirect method using a classical linear-quadratic-integral (LQI) controller with an integer-order boost converter reaches 98.38%, while the conventional direct-control P&O method achieves only 94.21%, demonstrating the superiority of the proposed fractional-order framework. Full article

Background/Objectives: Frailty and functional decline represent major challenges for aging populations, particularly among institutionalized older adults. Preserving functional capacity is essential to maintain autonomy, mobility, and quality of life. This study aimed to compare the effects of two strength training interventions—functional electromechanical dynamometer (FEMD) training and weighted vest training—on peak concentric and eccentric force during the sit-to-stand task, as well as on functional performance and body composition in frail nursing home residents. Methods: A pilot quasi-experimental study with a non-randomized control group was conducted in 19 older adults (mean age: 86.3 ± 5.8 years). Participants were allocated to FEMD training (EG1, n = 6), weighted vest training (EG2, n = 6), or a control group (CG, n = 7). Training was performed twice weekly for eight weeks. Assessments included body composition, handgrip strength, 30 s chair stand test, 3 m walking speed, and peak concentric and eccentric force during the sit-to-stand movement. Data were analyzed using mixed-model ANOVA and complementary within-group analyses. Results: No significant group × moment interactions were observed. However, EG1 demonstrated significant within-group improvements in chair stand performance (+4.8 repetitions, p = 0.006), walking speed (+0.1 m·s⁻¹, p = 0.030), concentric peak force (+46.5%, p = 0.008), and eccentric peak force (+34%, p = 0.047). EG2 showed a smaller but significant increase in eccentric peak force (+6.1%, p = 0.019), without functional improvements. Body composition changes were modest, with EG1 showing increases in weight and BMI without concomitant fat mass gains. Conclusions: In this pilot quasi-experimental study, functional electromechanical dynamometer-based training was associated with improvements in neuromuscular performance, particularly concentric peak force. However, no significant group × moment interactions were observed, indicating that differential effects between interventions cannot be established. Functional improvements should be interpreted cautiously. The present results should therefore be considered exploratory and hypothesis-generating. These findings suggest that FEMD-based training may be a feasible and potentially beneficial functional strength training strategy for frail institutionalized older adults, which should be confirmed in adequately powered randomized controlled trials. Full article

This paper presents a comprehensive characterization of bottom ash generated during biomass combustion in fluidized boilers, with a focus on its potential use in a circular economy. Two biomass bottom ash samples (BBA 1 and BBA 2) from commercial combined heat and power plants were tested. The scope of this study included the determination of chemical composition, phase composition, and leachability testing of selected impurities. The results showed that the bottom ashes tested are calcium silicate materials with varying proportions of calcium phases (anhydrite, portlandite, and calcite) and silica phases (quartz), depending on the type of biomass and combustion technology. Thermal analysis confirmed the presence of characteristic dehydration, decarbonation, and polymorphic transformations of quartz, with a low organic content. Leachability tests showed low mobility of most trace elements and heavy metals, with increased solubility of sulfates, chlorides, and alkali ions, typical for fluidized ash. The concentrations of As, Cd, Cr, Cu, Pb, Zn, and Hg in the eluates were low or below the limit of quantification, indicating the favorable chemical stability of the tested waste. The results obtained suggest that bottom ashes from biomass combustion in fluidized boilers may be a promising secondary raw material for engineering applications, especially in binding materials and bonded layers, and potentially also in selected agricultural applications, provided that the contents of sulfates, chlorides, and pH are controlled. Full article

Background/Objectives: Enhancing excipient functionality through environmentally friendly and scalable processing methods is essential for improving the manufacturability and performance of orally disintegrating tablets (ODTs). Microwave-assisted wet granulation enables controlled microstructural modification without chemical alteration of excipient components. This study aimed to develop and evaluate a rice starch (RS)–mannitol co-processed excipient using microwave-assisted wet granulation for direct compression of ODTs. Methods: RS and mannitol were co-processed by wet granulation followed by microwave treatment under varying power levels and irradiation times. The effects of processing conditions on granule morphology, solid-state properties, porosity, powder flow, compressibility, wettability, and disintegration behavior were systematically investigated. The optimized excipient was further evaluated in ODT formulations containing chlorpheniramine maleate and piroxicam and benchmarked against a commercial co-processed excipient (Starlac^®). Results: Microwave treatment generated internal vapor pressure that promoted pore formation and particle agglomeration, resulting in enhanced powder flowability (compressibility index 8.4–10.8%). Partial crystallinity reduction and microstructural modification improved compressibility and surface wettability compared with non-microwave-treated materials. The optimized formulation (MW-RM-H-30) exhibited rapid wetting (25 s), high water absorption (90.5%), low contact angle (42°), and fast tablet disintegration (31 s). ODTs prepared with MW-RM-H-30 showed rapid disintegration (42 s for chlorpheniramine maleate and 32 s for piroxicam) and dissolution behavior comparable to Starlac^®. Conclusions: Microwave-assisted wet granulation provides an efficient, scalable, and environmentally friendly strategy for engineering starch-based co-processed excipients with enhanced functionality for direct compression ODT applications. The developed excipient demonstrates strong potential for solid dosage form manufacturing. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

This study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the αβ/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4–0.8 p.u. On the forecasting side, a CEEMDAN-based decomposition–modeling–reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer–LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R² compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within ±3% of nominal, shortens re-synchronization from ≈0.35 s to ≈0.15 s, reduces rotor-current peaks by ≈5.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

With the growing penetration of renewable energy, automatic generation control (AGC) faces challenges like frequent frequency fluctuations and tie-line power deviations. Traditional proportional (PROP) allocation algorithms, limited by fixed weights, struggle to adapt to dynamic system changes. To address this, this study proposes a coordinated AGC allocation framework fusing a dual-flow Gate Recurrent Unit (GRU) with residual Multilayer Perceptron (MLP) based on PROP, preserving physical prior knowledge while learning adaptive correction terms. Validated on a provincial power grid, the proposed method reduces the cumulative absolute ACE (Sum) by about 0.3–0.9% compared with PROP under 10–100 MW step disturbances. Under random disturbances, it achieves larger reductions of about 3.2% (vs. PROP) and 4.8% (vs. MLP), while maintaining interpretability and deployment feasibility, improving the relevant performance indicators of AGC unit allocation while maintaining interpretability and deployment feasibility, providing an effective solution for AGC under high renewable energy penetration. Full article