Search Results (6,603)

With the increasing penetration of renewable energy, the stochastic and intermittent nature of its generation increases operational uncertainty and vulnerability, posing significant challenges for grid stability. However, traditional algorithms typically identify critical nodes by focusing solely on the network topology or power flow, or by combining the two, which leads to the inaccurate and incomplete identification of essential nodes. To address this, we propose the Three-Dimensional Value-Based Gravity Model (3V-GM), which integrates structural and electrical–physical attributes across three layers. In the plane layer, we combine each node’s global topological position with its real-time supply–demand voltage state. In the line layer, we introduce an electrical coupling distance to quantify the strength of electromagnetic interactions between nodes. In the point layer, we apply eigenvector centrality to detect latent hub nodes whose influence is not immediately apparent. The performance of our proposed method was evaluated by examining the change in the load loss rate as nodes were sequentially removed. To assess the effectiveness of the 3V-GM approach, simulations were conducted on the IEEE 39 system, as well as six other benchmark networks. The simulations were performed using Python scripts, with operational parameters such as bus voltages, active and reactive power flows, and branch impedances obtained from standard test cases provided by MATPOWER v7.1. The results consistently show that removing the same number of nodes identified by 3V-GM leads to a greater load loss compared to the six baseline methods. This demonstrates the superior accuracy and stability of our approach. Additionally, an ablation experiment, which decomposed and recombined the three layers, further highlights the unique contribution of each component to the overall performance. Full article

The rapid shift toward electric vehicles (EVs) has underscored the critical importance of battery pack crashworthiness, creating a demand for lightweight, energy-absorbing protective systems. This review systematically explores bio-inspired cellular structures as promising solutions for improving the impact resistance of EV battery packs. Inspired by natural geometries, these designs exhibit superior energy absorption, controlled deformation behavior, and high structural efficiency compared to conventional configurations. A comprehensive analysis of experimental, numerical, and theoretical studies published up to mid-2025 was conducted, with emphasis on design strategies, optimization techniques, and performance under diverse loading conditions. Findings show that auxetic, honeycomb, and hierarchical multi-cell architectures can markedly enhance specific energy absorption and deformation control, with improvements often exceeding 100% over traditional structures. Finite element analyses highlight their ability to achieve controlled deformation and efficient energy dissipation, while optimization strategies, including machine learning, genetic algorithms, and multi-objective approaches, enable effective trade-offs between energy absorption, weight reduction, and manufacturability. Persistent challenges remain in structural optimization, overreliance on numerical simulations with limited experimental validation, and narrow focus on a few bio-inspired geometries and thermo-electro-mechanical coupling, for which engineering solutions are proposed. The review concludes with future research directions focused on geometric optimization, multi-physics modeling, and industrial integration strategies. Collectively, this work provides a comprehensive framework for advancing next-generation crashworthy battery pack designs that integrate safety, performance, and sustainability in electric mobility. Full article

High-voltage transmission lines are the backbone of modern power systems, facilitating the delivery of electricity from diverse generation sources, including conventional power plants and renewable energy systems, to consumers. As the electricity demand grows, the expansion of transmission infrastructure becomes essential to connecting new consumers with power suppliers. However, traditional transmission lines require significant right-of-way, posing challenges related to land use and environmental impact, as well as limited loadability. To address this issue, compact unconventional High Surge Impedance Loading (HSIL) transmission lines offer a viable solution by reducing right-of-way requirements while enhancing line natural power, mainly leading to less voltage drop. Before the implementation of the new unconventional HSIL lines, it is crucial to assess key parameters, such as magnetic field distribution under the lines, to ensure compliance with environmental and safety standards. This paper presents a numerical analysis of the magnetic field characteristics of compact unconventional HSIL transmission lines with different subconductor configurations. The results show that the proposed HSIL designs can reduce the magnetic field at ground level by up to 71.74% compared to a conventional 500 kV line near the center, as well as by up to 74% at the right-of-way edge, while maintaining magnetic field levels well below the limits set by ICNIRP and state-specific regulations. This study evaluates the magnetic field distribution within the right-of-way, providing insights into the electromagnetic performance and potential implications for transmission line design. Full article

Introduction: Overuse injuries are a growing concern among adolescent soccer players, with the repetitive nature of the sport placing significant physical demands on young athletes. These injuries can have long-term implications for physical development, performance, and overall well-being. This narrative synthesis aimed to evaluate the existing literature on the epidemiology, risk factors, and management strategies for overuse injuries in adolescent soccer players. Methods: A comprehensive literature search was conducted using PubMed and Embase. A total of 123 articles were identified, 27 of which met the inclusion criteria after screening. Studies focusing on overuse injuries in adolescent soccer players aged 10–18 years were included, while those addressing acute injuries, non-soccer populations, or adult athletes were excluded. Relevant quantitative and qualitative data were extracted and evaluated. Due to heterogeneity in study designs and outcomes, findings were narratively synthesized rather than meta-analyzed. Results: The period around peak height velocity (PHV: 11.5 years in girls, 13.5 years in boys) was consistently identified as a high-risk window, with seven studies demonstrating a significantly increased incidence of overuse injuries. Additional risk factors included leg length asymmetry, truncal weakness, early sport specialization, high ratios of organized-to-free play, and increased body size. Injury burden was greatest for hamstring and groin injuries, often leading to prolonged time lost from play. Preventive interventions such as plyometric training, trunk stabilization, and structured load monitoring demonstrated reductions in injury incidence in several prospective studies, though protocols varied widely. Conclusion: This narrative synthesis highlights PHV as the most consistent risk factor for overuse injuries in adolescent soccer players, alongside modifiable contributors such as training load, sport specialization, and free play balance. Evidence supports neuromuscular training and structured monitoring as promising preventive strategies, but there remains a lack of standardized, evidence-based protocols. Future research should focus on optimizing and validating interventions, integrating growth and load monitoring, and leveraging emerging approaches such as machine learning-based risk prediction. Full article

This study evaluates the technical, economic, and environmental feasibility of grid-connected and off-grid hybrid energy systems designed to meet the energy demands of a greenhouse facility. Various system configurations were developed based on combinations of solar, wind, diesel, and battery storage technologies. The analysis considers a daily electricity consumption of 369.52 kWh and a peak load of 52.59 kW for the greenhouse complex. Among the grid-connected systems, the grid/PV configuration was identified as the most optimal, offering the lowest Net Present Cost (NPC) of USD 282,492, the lowest Levelized Cost of Energy (LCOE) at USD 0.0401/kWh, and a reasonable emissions reduction of 54.94%. For off-grid scenarios, the generator/PV/battery configuration was the most cost-effective option, with a total cost of USD 1.19 million and an LCOE of USD 0.342/kWh. Environmentally, this system showed a strong performance, achieving a 64.58% reduction in CO₂ emissions; in contrast, fully renewable systems such as PV/wind/battery and wind/battery configurations succeeded in reaching zero-emission targets but were economically unfeasible due to their very high investment costs and limited practical applicability. Sensitivity analyses revealed that economic factors such as inflation and energy prices have a critical effect on the payback time and the Internal Rate of Return (IRR). Full article

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A novel two-stage P2G, replacing traditional devices with electrolysers (EL), methane reactors (MR), and hydrogen fuel cells (HFC), enhances energy efficiency and facilitates the utilisation of captured carbon. Furthermore, adjustable thermoelectric ratios in combined heat and power (CHP) and HFC improve both economic and environmental performance. A ladder-type carbon trading and green certificate trading mechanism is introduced to effectively manage carbon emissions. To address the uncertainties in supply and demand, the study applies information gap decision theory (IGDT) and develops a robust risk-averse model. The results from various operating scenarios reveal the following key findings: (1) the integration of CCT with the two-stage P2G system increases renewable energy consumption and reduces carbon emissions by 5.8%; (2) adjustable thermoelectric ratios in CHP and HFC allow for flexible adjustment of output power in response to load requirements, thereby reducing costs while simultaneously lowering carbon emissions; (3) the incorporation of ladder-type carbon trading and green certificate trading reduces the total cost by 7.8%; (4) in the IGDT-based robust model, there is a positive correlation between total cost, uncertainty degree, and the cost deviation coefficient. The appropriate selection of the cost deviation coefficient is crucial for balancing system economics with the associated risk of uncertainty. Full article

Traditional pseudo-static loading tests fail to capture the unique characteristics of special ground motions, limiting their ability to accurately evaluate the seismic performance of steel-reinforced concrete (SRC) columns. In this study, eight SRC columns were subjected to pseudo-static tests using far-field, near-field, and traditional loading protocols to investigate their structural response under different seismic scenarios. The results show that far-field loading, characterized by repeated large displacement cycles, leads to increased damage accumulation, reduced hysteresis curve fullness, greater bearing capacity loss, significant stiffness degradation, and diminished ductility and energy dissipation. In contrast, near-field loading—dominated by an initial extreme displacement—results in fewer but less developed cracks and a larger concrete crushed zone at failure. The severe initial damage under near-field loading causes a noticeable decline in stiffness and strength during subsequent cycles. During the second loading stage, both the peak load and post-peak deformation capacity are further reduced, significantly impairing the columns’ ability to resist additional seismic demands. These findings highlight the critical role of loading history in shaping the seismic behavior of SRC composite columns. Full article

This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors. Full article

During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions. Full article

To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and reduce electricity bills. Instead of installing a sensing device on each electric appliance, non-intrusive load monitoring (NILM) enables the monitoring of each individual device using the total power reading of the home smart meter. However, for a high-accuracy load monitoring, efficient artificial intelligence (AI) and deep learning (DL) approaches are needed. To that end, this paper thoroughly reviews traditional AI and DL approaches, as well as emerging AI models proposed for NILM. Unlike existing surveys that are usually limited to a specific approach or a subset of approaches, this review paper presents a comprehensive survey of an ensemble of topics and models, including deep learning, generative AI (GAI), emerging attention-enhanced GAI, and hybrid AI approaches. Another distinctive feature of this work compared to existing surveys is that it also reviews actual cases of NILM system design and implementation, covering a wide range of technical enablers including hardware, software, and AI models. Furthermore, a range of new future research and challenges are discussed, such as the heterogeneity of energy sources, data uncertainty, privacy and safety, cost and complexity reduction, and the need for a standardized comparison. Full article

Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors. Full article

The rapid expansion of broadband wireless communication systems, including 5G, satellite networks, and next-generation IoT platforms, has created a strong demand for antenna architectures capable of real-time beam control, compact integration, and broad frequency coverage. Traditional reflectarrays, while effective for narrowband applications, often face inherent limitations such as fixed beam direction, high insertion loss, and complex phase-shifting networks, making them less viable for modern adaptive and reconfigurable systems. Addressing these challenges, this work presents a novel wideband planar metasurface that operates as a polarization rotation reflective metasurface (PRRM), combining 90° polarization conversion with 1-bit reconfigurable phase modulation. The metasurface employs a mirror-symmetric unit cell structure, incorporating a cross-shaped patch with fan-shaped stub loading and integrated PIN diodes, connected through vertical interconnect accesses (VIAs). This design enables stable binary phase control with minimal loss across a significantly wide frequency range. Full-wave electromagnetic simulations confirm that the proposed unit cell maintains consistent cross-polarized reflection performance and phase switching from 3.83 GHz to 15.06 GHz, achieving a remarkable fractional bandwidth of 118.89%. To verify its applicability, the full-wave simulation analysis of a 16 × 16 array was conducted, demonstrating dynamic two-dimensional beam steering up to ±60° and maintaining a 3 dB gain bandwidth of 55.3%. These results establish the metasurface’s suitability for advanced beamforming, making it a strong candidate for compact, electronically reconfigurable antennas in high-speed wireless communication, radar imaging, and sensing systems. Full article

The permanent magnetic synchronous motor (PMSM) is of significant use for the centrifugal hydrogen compressor (CHC) in the hydrogen fuel cell system. In order to satisfy the demand for improving the CHC’s performance, including higher accuracy, higher response speed, and wider speed range, this paper proposes a novel adaptive super-twisting sliding mode observer (ASTSMO)-based position sensorless control strategy for the highspeed PMSM. Firstly, the super-twisting algorithm (STA) is introduced to the sliding mode observer (SMO) to reduce chattering and improve the accuracy of position estimation. Secondly, to increase the convergence speed, the ASTSMO is extended with a linear correction term, where an extra proportionality coefficient is used to adjust the stator current error under dynamic operation. Finally, a novel adaptive law is designed to solve the PMSM’s problems of wide speed change, wide current variation, and inevitable parameters fluctuation, which are caused by the CHC’s complex working environment like frequent load changes and significant temperature variations. In the experimental verification, the position accuracy and dynamic performance of the PMSM are both improved. It is also proved that the proposed strategy can guarantee the stable operation and fast response of the CHC, so as to maintain the reliability and the hydrogen utilization of the hydrogen fuel cell system. Full article

Electro-hydraulic servoactuators have great potential in mobile robotics due to their robustness, high bandwidth and power density, but compared with electromechanical actuators, they can be inefficient and more difficult to integrate into systems. The Integrated Smart Actuator (ISA) developed by Moog Controls Ltd. is a hydromechatronic device that aims to address these issues by combining a novel efficient servovalve, cylinder, sensors and control electronics into a single component. The aim of this work was to develop a robust motion control algorithm that can make integration of the ISA into a robotic system straightforward by requiring minimal controller set-up despite variations in the load characteristics. The proposed controller is a sliding mode controller with a varying boundary layer that contains two robustness parameters and a single bandwidth parameter that defines the response. The controller outperforms a conventional high-performance linear controller in terms of tracking performance and its robustness to variations in the load mass and fluid bulk modulus. The response when the system was subject to some unachievable demand trajectories, such as large step demands, was found to be poor, and an online velocity, acceleration and jerk limited trajectory filter was demonstrated to rectify this issue. The successful implementation of a robust motion controller enables this highly novel integrated actuator to live up to its ‘smart’ epithet. Full article

The air transportation industry is challenged to address airport curbside delay problems that affect landside service quality and can potentially impact check-in operations. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of the level of service and capacity of the curbside processor. A key component of the method is the simulation of the stochastic balance of demand and available curb space for unloading/loading tasks using the Monte Carlo simulation model. The method meets the planning and operation requirements with the ability to analyze conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys. Full article