Search Results (77)

Accurate accounting of residual film recovery operation areas is essential for supporting targeted implementation of white pollution control policies in cotton fields and serves as a critical foundation for data-driven prevention and control of soil contamination. To address the reliance on manual screening during preprocessing in traditional residual film recovery area calculation methods, this study proposes a DBSCAN-MFI field-road trajectory segmentation method. This approach combines DBSCAN density clustering with multi-feature inference. Building on DBSCAN clustering, the method incorporates a convex hull completion strategy and multi-feature inference rules utilizing speed-direction feature filtering to automatically identify and segment field and road areas, enabling precise operation area calculation. Experimental results demonstrate that compared to DBSCAN, OPTICS, the Grid-Based Method, and the DBSCAN-FR algorithm, the proposed algorithm improves the F1-Score by 7.01%, 7.13%, 7.28%, and 4.27%, respectively. Regarding the impact on operation area calculation, segmentation accuracy increased by 23.61%, 25.14%, 20.71%, and 6.87%, respectively. This study provides an effective solution for accurate field-road segmentation during mechanical residual film recovery operations to facilitate subsequent calculation of the recovered area. Full article

Quantifying methane (CH₄) emissions is essential for climate change mitigation; however, current estimation methods often suffer from substantial uncertainties, particularly at the site level. This study introduces a drone-based approach for measuring CH₄ emissions using an open-path Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor mounted parallel to the ground, rather than in the traditional nadir-pointing configuration. Controlled CH₄ release experiments were conducted to evaluate the method’s accuracy, employing a modified mass-balance technique to estimate emission rates. Two wind data processing strategies were compared: a logarithmic wind profile (LW) and a constant scalar wind speed (SW). The LW approach yielded highly accurate results, with an average recovery rate of 98%, while the SW approach showed greater variability with increasing distance from the source, although it remained reliable in close proximity. The method demonstrated the ability to quantify emissions as low as 0.08 g s⁻¹ with approximately 4% error, given sufficient sampling. These findings suggest that the proposed UAV-based system is a promising, cost-effective tool for accurate CH₄ emission quantification in sectors, such as agriculture, energy, and waste management, where traditional monitoring techniques may be impractical or limited. Full article

Objectives: This study aimed to comprehensively investigate the physiological effects of omega-3 fatty acid supplementation combined with resistance training on the lipid profile, inflammatory and antioxidant responses, neuro-biomarkers, and physical performance parameters in physically healthy young adults. Methods: Thirty physically active male participants were randomly assigned to an experimental group (omega-3 + resistance training) or a control group (resistance training only). Over eight weeks, both groups performed a standardized resistance training program three times per week. The experimental group additionally received 3150 mg/day of omega-3 fatty acids (EPA and DHA). Pre- and post-intervention assessments included blood biomarkers (LDL, HDL, triglycerides, IL-6, TNF-α, CRP, GSH, MDA, BDNF, serotonin, and dopamine) and physical performance tests (1RM, CMJ, RSI, 10 m sprint, and Illinois agility). Results: The experimental group showed significant improvements in the lipid profile, with decreases in LDL and triglyceride levels and an increase in HDL levels. Levels of the inflammatory cytokines IL-6 and TNF-α were significantly reduced, while GSH levels increased and MDA levels decreased, indicating an enhanced antioxidant status. The neuro-biomarker analysis revealed increased levels of BDNF, dopamine, and serotonin. Physical performance tests demonstrated greater improvements in muscular strength, power, speed, agility, and reaction-based performance in the omega-3 group compared to controls. Conclusions: These findings suggest that omega-3 supplementation, when combined with resistance training, has a multi-systemic enhancing effect on both physiological markers and physical performance. This combination may represent a promising strategy for optimizing athletic adaptations and recovery in physically active populations. Future studies should further explore these effects across different populations and training modalities. Full article

To enhance the speed control performance of the permanent magnet synchronous motor (PMSM) servo system, an improved sliding mode control method integrating a torque observer is presented. The current loop uses current feedback decoupling PID control, and the speed loop applies sliding mode control. In comparison to previous work in hybrid SMC using fuzzy logic and torque observers, this p proposes a hyperbolic tangent function in replacement of the signum function to solve the conflict between rapidity and chattering in the traditional exponential reaching law, and fuzzy and segmental self-tuning rules adjust relevant switching terms to reduce chattering and improve the sliding mode arrival process. A load torque observer is designed to enhance the system’s anti-interference ability by compensating the observed load torque to the current loop input. Simulation results show that compared with traditional sliding mode control with a load torque observer (SMC + LO), PID control with a load torque observer (PID + LO), and Active Disturbance Rejection Control (ADRC), the proposed strategy can track the desired speed in 0.032 s, has a dynamic deceleration of 2.7 r/min during sudden load increases, and has a recovery time of 0.011 s, while the others have relatively inferior performance. Finally, the model experiment is carried out, and the results of the experiment are basically consistent with the simulation results. Simulation and experimental results confirm the superiority of the proposed control strategy in improving the system’s comprehensive performance. Full article

Driven by the demand for low-carbon and sustainable development, power systems are increasingly transitioning toward higher proportions of renewable energy and power-electronic interfaces, leading to a growing requirement for wind turbines to provide inertia support and frequency regulation (FR). Wind turbine kinetic energy-based FR inherently involves a trade-off between rotor speed recovery and grid stability: aggressive acceleration exacerbates the secondary frequency drop (SFD), while suppressing SFD prolongs rotor speed recovery. This study aims to resolve this dynamic coupling conflict and optimize the rotor speed recovery process by employing a segmented rotor speed recovery strategy. Firstly, a detailed wind farm-integrated frequency response model is developed. Leveraging its identified speed recovery dynamics, a five-dimensional rotor speed recovery evaluation framework is established. Subsequently, guided by this evaluation framework, a segmented rotor speed recovery control strategy is designed. Finally, three validation scenarios—a single wind turbine, 10% wind power penetration, and 30% wind power penetration—are constructed to evaluate the proposed strategy. Comparative analysis demonstrates that the proposed segmented rotor speed recovery strategy reduces aerodynamic power recovery time by 28.5% and power disturbance by 47.3% in an operational scenario with 30% wind power penetration, effectively achieving synergistic coordination of recovery acceleration and SFD suppression. Full article

Single-phase short-circuit faults are severe asymmetrical fault modes in high renewable energy power systems. They can easily cause large-scale renewable energy to enter the low-voltage ride-through (LVRT) state. When such symmetrical or asymmetrical faults occur in the transmission channels of high-proportion wind power clusters, they may trigger the tripping of thermal power units and a transient voltage drop in most wind turbines in the high-proportion wind power area. This causes an instantaneous active power deficiency and poses a low-frequency oscillation risk. To address the deficiencies of wind turbine units in fault ride-through (FRT) and active frequency regulation capabilities, a power emergency support scheme for wind power clusters based on doubly fed variable-speed pumped storage dynamic excitation is proposed. A dual-channel energy control model for variable-speed pumped storage units is established via AC excitation control. This model provides inertia support and FRT energy simultaneously through AC excitation control of variable-speed pumped storage units. Considering the transient stability of the power network in the wind power cluster transmission system, this scheme prioritizes offering dynamic reactive power to support voltage recovery and suppresses power oscillations caused by power deficiency during LVRT. The electromagnetic torque completed the power regulation within 0.4 s. Finally, the effectiveness of the proposed strategy is verified through modeling and analysis based on the actual power network of a certain region in Northeast China. Full article

Large-inertia pump-controlled grinding machines experience significant energy loss and potential hydraulic shock during frequent high-speed table reciprocation. Traditional control methods often neglect to address efficient energy recovery during the dynamic commutation phase. This study proposes and investigates a dual-mode synergistic energy recovery system that combines motor regeneration and accumulator storage for pump-controlled grinders. The primary focus of this study is on developing a maximum regenerative energy commutation control strategy. A mathematical model of the system was established, and extensive simulations were performed to analyze the energy recovery process under varying load mass, initial velocity, and leakage coefficient conditions. Machine learning models were compared for predicting the peak time of total recovered energy, with a neural network (NN) demonstrating superior accuracy (R² ≈ 0.99997). An adaptive commutation strategy was designed, utilizing the NN prediction corrected by a confidence score based on historical and test data ranges, to determine the optimal moment for initiating reverse motion. The strategy was validated using Simulink–Amesim co-simulation and experiments conducted on a 10-ton test bench. The results show that the proposed strategy effectively maximizes energy capture; experiments indicate a 14.3% increase in energy recovery efficiency and a 25% reduction in commutation time compared to a fixed timing approach. The proposed commutation strategy also leads to faster settling to steady-state velocity and smoother operation, while the accumulator demonstrably reduces pressure peaks. This research provides a robust method for enhancing energy efficiency and productivity in pump-controlled grinding applications by improving regenerative braking control through a predictive commutation strategy. Full article

To ensure the reliability and security of Colombia’s national power system, there is an ongoing necessity for upgrades in monitoring and protection mechanisms. Approximately sixteen years ago, the introduction of synchrophasor measurements enabled the swift detection of potentially network-detrimental events. Subsequent advancements have seen the deployment of Phasor Measurement Units (PMUs), currently tallying 150 across 25 substations, facilitating real-time monitoring and analysis. The growth of the PMU network is pivotal for the modernization of the National Control Center, particularly in the face of complexities introduced by renewable energy sources. There is an increasing demand for data analytics platforms to support operators in responding to threats. This paper explores the development of the Colombian Wide-Area Measurement System (WAMS) network, highlighting its milestones and advancements. Significant contributions include the technological evolution of the WAMS for real-time monitoring, an innovative high-speed data analytics strategy, and tools for the monitoring of frequency, rate of change of frequency (RoCoF), angular differences, oscillations, and voltage recovery, alongside industry-specific criteria for real-time assessment. Implemented within an operational WAMS, these tools enhance situational awareness, thereby assisting operators in decision-making and augmenting the power system’s reliability, security, and efficiency, underscoring their significance in modernization and sustainability initiatives. Full article

The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportation. Dynamic wireless charging systems, which enable EVs to charge while in motion, offer a transformative approach to mitigating range anxiety and optimizing energy utilization. However, these systems face significant operational challenges, including dynamic traffic conditions, uncertain EV arrival patterns, energy transfer efficiency variations, and renewable energy intermittency. This paper proposes a novel quantum computing-assisted optimization framework for the modeling, operation, and control of wireless dynamic charging infrastructure in urban highway networks. Specifically, we leverage Variational Quantum Algorithms (VQAs) to address the high-dimensional, multi-objective optimization problem associated with real-time energy dispatch, charging pad utilization, and traffic flow coordination. The mathematical modeling framework captures critical aspects of the system, including power balance constraints, state-of-charge (SOC) dynamics, stochastic vehicle arrivals, and charging efficiency degradation due to vehicle misalignment and speed variations. The proposed methodology integrates quantum-inspired optimization techniques with classical distributionally robust optimization (DRO) principles, ensuring adaptability to system uncertainties while maintaining computational efficiency. A comprehensive case study is conducted on a 50 km urban highway network equipped with 20 charging pad segments, supporting an average traffic flow of 10,000 EVs per day. The results demonstrate that the proposed quantum-assisted approach significantly enhances energy efficiency, reducing energy losses by up to 18% compared to classical optimization methods. Moreover, traffic-aware adaptive charging strategies improve SOC recovery by 25% during peak congestion periods while ensuring equitable energy allocation among different vehicle types. The framework also facilitates a 30% increase in renewable energy utilization, aligning energy dispatch with periods of high solar and wind generation. Key insights from the case study highlight the critical impact of vehicle alignment, speed variations, and congestion on wireless charging performance, emphasizing the need for intelligent scheduling and real-time optimization. The findings contribute to advancing the integration of quantum computing into sustainable transportation planning, offering a scalable and robust solution for next-generation EV charging infrastructure. Full article

The regenerative potential of mesenchymal stem cell (MSC) secretomes in peripheral nerve injuries warrants rigorous evaluation. This systematic review analyzes their effectiveness in preclinical models of neurotmesis, a complete transection of a nerve. Neurophysiological recovery was assessed through nerve conduction velocity (NCV), a measure of the speed at which electrical impulses travel along a nerve. Following PRISMA guidelines, a systematic search was conducted in PubMed, Scopus, Web of Science, and ScienceDirect (last search July 2024). From 640 initially identified studies, 13 met inclusion criteria, encompassing 514 animals (rats). experimental designs published since 2014 in English or Spanish, focusing on MSC secretomes for nerve regeneration. Exclusion criteria included reviews, case reports, and incomplete data. The risk of bias was assessed using Joanna Briggs Institute tools. Results were synthesized narratively, focusing on functional and structural outcomes. The included studies employed various MSC sources, including adipose tissue, olfactory mucosa, and umbilical cord. Nine studies reported enhanced SFI, favoring secretome-treated groups over controls (mean difference +20.5%, p < 0.01). Seven studies documented increased NCV, with up to 35% higher conduction velocities in treated groups (p < 0.05). Histological outcomes reported in 12 studies showed increased axonal diameter (+25%, p < 0.01), myelin sheath thickness (+30%, p < 0.05), and Schwann cell proliferation. Limitations of the included evidence include methodological heterogeneity and variability in outcome measurement tools. MSC-derived secretomes demonstrate potential as advanced therapeutic strategies for nerve injuries. Personalized approaches considering injury type and clinical context are essential for optimizing outcomes. Full article

(1) Background: Complex training combines weight training and plyometric exercises within one series. This is one of the first systematic reviews to thoroughly investigate the effects of complex training on the motor abilities of male basketball players. Therefore, this systematic review aimed to determine the effects of complex training on the motor abilities of male basketball players; (2) Methods: The study protocol of this systematic review was registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202520116). Papers published from January 2008 to October 2024 were searched digitally using the PubMed, Web of Science, Scopus, MEDLINE, ERIC, and Google Scholar databases following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the PICOS strategy. The Methodological Index for Non-Randomized Studies (MINORS) and Physical Therapy Database (PEDro) scale was used to assess the quality of the included randomized and non-randomized control trials, respectively; (3) Results: The results of this study showed that complex training is an effective method for improving the motor abilities of basketball players. Most studies investigating the effects of complex training have focused on explosive strength, where a positive impact has been demonstrated. In addition, studies show the positive effects of complex training on speed, agility, muscle strength, isometric muscle force, and aerobic endurance; (4) Conclusions: The authors of the study recommend that the most effective programs are 10 weeks long and conducted 2–3 times per week, with at least 48 h of recovery between sessions. For the pre-competition period, it is recommended to perform 3–5 sets of one complex pair; 2–12 repetitions of weight training, (70–95% 1RM); and 5–15 repetitions of plyometric exercises which are performed at maximum intensity. Future researchers in the field of basketball are encouraged to compare the effects of complex training in male and female basketball players or to compare the effects among male basketball players based on age (juniors vs. seniors) and competitive level (amateur vs. elite). Full article

Modular multilevel converter–high-voltage direct current (MMC-HVDC) systems are a key technology for integrating large-scale offshore wind farms due to their flexibility, controllability, and decoupled active and reactive power characteristics. However, offshore wind farms rely on power electronic converters, resulting in low inertia, which can worsen frequency fluctuations and affect system stability during major disturbances. Additionally, the decoupled power control of MMC-HVDC systems limits wind farms’ inertia contribution to the AC grid, exacerbating inertia deficiency. To address this, a coordinated inertia support strategy is proposed, utilizing a DC voltage–frequency mapping method that enables wind farms to perceive frequency variations without communication and rapidly provide inertia response. This strategy coordinates wind farms and MMC-HVDC systems to enhance frequency support. Simulations demonstrate that the proposed strategy overcomes MMC-HVDC’s decoupling effect, accelerates frequency recovery, and improves the inertia response speed, achieving faster power support and higher peak power output, thereby enhancing frequency stability. Furthermore, PSCAD/EMTDC simulations were conducted to analyze the transient characteristics of MMC-HVDC under AC-side faults, verifying that braking resistors (BRs) effectively suppress DC overvoltage, reducing wind farm power curtailment and improving system security. This study provides a new approach for frequency stability control in MMC-HVDC-based offshore wind integration and serves as a reference for further optimization of inertia support and fault protection strategies. Full article

This paper proposes a grid-forming (GFM) photovoltaic system transient control strategy based on the combination of dynamic virtual impedance and the radial basis function (RBF) algorithm. First, the virtual synchronous generator (VSG) model is analyzed to understand how virtual impedance affects current surges and system stability during faults. By using dynamic virtual impedance throughout the fault, the strategy suppresses current spikes and improves stability. The RBF neural network dynamically adjusts virtual inertia and damping coefficients to optimize transient power-angle characteristics and speed up recovery during fault restoration. Simulation results show that the strategy reduces transient current surges, improves angle recovery, and boosts system stability during voltage sag. This approach offers an effective solution for low-voltage ride-through (LVRT) and transient control in photovoltaic grid-connected systems, ensuring the resilience and stability of renewable energy integration into the grid. Full article

Variable-Speed Hydropower Plants (VSHP) are becoming more promising for stabilising power grids with the increasing integration of renewable energy sources. This research focuses on improving fault ride-through capabilities and delivering efficient ancillary services for VSHPs to support the grid by developing a comprehensive control strategy. The control system proposed integrates a machine-side controller, a Frequency Support Controller (FSC), a Virtual Synchronous Machine (VSM), a Vector Current Controller (VCC) for the grid-side converter, a turbine governor for regulating turbine speed, and a DC-link controller. PID with an anti-windup scheme and a Model Predictive Controller (MPC) were employed for the turbine governor. The MPC turbine governor results demonstrate the potential of advanced control methods for enhanced performance of the VSHP. A benchmarking between the MPC and the PID governor was made. The benchmarking results have reported that the MPC can achieve reference tracking improvements up to $99.42\%$. Tests on a diverse set of grid scenarios were conducted, and the graphical results showed significant improvements in mitigating the frequency drops through the effective governor response. The synthetic inertia provision is swift, completing within seconds of a frequency drop. Compared to the fixed-speed approach, the VSHP improves the grid’s overall stability by minimising frequency dipping and achieving steady-state recovery remarkably faster. The fixed-speed approach only begins to recover minutes after the VSHP reaches the settling time. By effectively providing critical ancillary services such as frequency support, synthetic inertia, and smooth fault ride-through capability, the VSHP can become a transformative solution for future power grids, which are estimated to be more reliant on renewable energy sources. Full article

Given the shortcomings of compressed air energy storage systems in emergency response in power auxiliary research, especially in the scenario of decoupling from the power grid, an in-depth analysis is conducted. A set of energy release stage models with 10 MW compressed air energy storage equipped with an anti-overspeed system are set up. This research mainly focuses on the speed control of the two stages of the decoupled compressed air energy storage system: the soaring speed and the system recovery standby. By analyzing the influence of different cut-off valve actions on the decoupled speed, it is concluded that the key factor of speed control is the isolated expander. After the speed is controlled, the main factors affecting the speed control in the system are analyzed. As long as the expander is cut off, the high-temperature and high-pressure air will remain in the internal pipe and the heat exchanger of the system, which will cause the speed of the generator to soar again. A new load rejection control strategy is proposed based on the above analysis, in which the speed is smoothly reduced to 3000 r/min by the cut-off valve at the front end of the expander, and the residual working fluid is discharged. The results show that the optimized load rejection strategy reduces the speed increment by 89% compared to the traditional strategy, and reduces the recovery standby practice by 65%. Under 75% load conditions, the optimized load rejection strategy reduces the speed increment by 87% and the recovery standby practice by 41% compared to the traditional strategy. At 50% load conditions, the optimized load rejection strategy reduces the speed increment and standby time by 86% and 33%, respectively, compared to the traditional strategy. The key speed control index of the optimized load rejection strategy is much better than the traditional strategy, which significantly improves the control effect of accident emergencies. Full article