Search Results (30,167)

Multilevel inverters (MLI) have become the frontier in high-power medium voltage systems because of their unique property of generating sinusoidal voltage through smaller voltage increments. Although many MLI structures have been proposed over the years, most still rely on a large number of switches, which increases complexity and conduction losses. In this work, a reconfigurable, gable-shaped multilevel inverter module, capable of operating in both symmetric and asymmetric modes, is introduced for use in AC microgrid cluster environments. The design employs five DC sources and six semiconductor devices arranged in a gable layout, which helps shorten the conduction path while also reducing the total hardware count. As a result, the inverter becomes more compact, experiences lower switching losses, and proves more suitable for grid-connected operation. In symmetric mode, the inverter delivers an 11-level output, while the asymmetric arrangement produces 19 levels. The proposed concept is examined through MATLAB/Simulink (R2023a) studies, and its practicality is verified using a Hardware-in-the-Loop setup with an integrated data-acquisition system capable of delivering 10 kW of real power and handling up to 50% overload. These results confirm the suitability of the topology for real-time grid applications. Full article

Direct current fast charging is a necessary element of the transition to electric vehicles (EVs). Regulatory complexity, capital requirements, and challenging business models hinder charging infrastructure deployment, so focusing on the efficient use of such infrastructure is of paramount importance. A tool to improve this efficiency is an incentive to terminate charging events when charging power drops, the vehicle state of charge rises above some value, or time plugged in exceeds a threshold. A timeseries charging demand model was built based on observed EV population and charging behavior. This was used to explore these three incentive trigger metrics across a range of plausible values, to find their relative impacts on the vehicles charging, those waiting in line to access a cordset, and charging site operators. Results indicate that basing such a trigger on charging power would have little impact if the threshold power is low enough to accommodate older, slower-charging vehicles, but that more restrictive limits based on state of charge or charging duration can decrease wait times, increase vehicle throughput, and increase total energy sales for cordsets serving more than 1000 EVs per year. Full article

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

Among wireless communication technologies underlying Internet of Things (IoT)-based smart buildings, IQRF (Intelligent Connectivity Using Radio Frequency) technology is a promising candidate due to its low power consumption, cost-effectiveness, and wide coverage. However, effectively modeling the propagation characteristics of IQRF in complex indoor environments for simple and accurate network deployment remains challenging, as architectural elements like walls and corners cause substantial signal attenuation and unpredictable propagation behavior. This study investigates the applicability of a site-specific modeling approach, originally developed for urban street canyons, to characterize peer-to-peer (P2P) IQRF links operating at 868 MHz in typical indoor scenarios, including line-of-sight (LoS), one-turn, and two-turn non-line-of-sight (NLoS) configurations. The received signal powers are compared with well-known empirical models, including international telecommunication union radio communication sector (ITU-R) P.1238-9 and WINNER II, and ray-tracing simulations. The results show that while ITU-R P.1238-9 achieves lower prediction error under LoS conditions with a root mean square error (RMSE) of 5.694 dB, the site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links. Furthermore, ray-tracing simulations exhibited notably larger deviations, with RMSE values ranging from 7.522 dB to 16.267 dB and lower correlation with measurements. These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments. Full article

Underground mine emergencies destroy communication infrastructure when situational awareness is most critical. Current systems rely on centralized network infrastructure, which fails during emergencies when miners are trapped and require rescue coordination. This paper proposes an energy-harvesting LoRa mesh network that addresses self-powered operation, interference management, and adaptive physical layer optimization under severe underground propagation conditions. A dual-antenna architecture separates RF energy harvesting (860 MHz) from LoRa communication (915 MHz), enabling continuous operation with supercapacitor storage. The core contribution is a decentralized scheduler that derives optimal timing offsets by modeling concurrent transmissions as a Poisson collision process, exploiting LoRa’s capture effect while maintaining network coherence. A SINR-aware physical layer adapts spreading factor, bandwidth, and coding rate with hysteresis, controls recomputing timing parameters after each change. Experimental validation in Missouri S&T’s operational mine demonstrates far-field wireless power transfer (WPT) reaching 35 m. Simulations across 2000 independent trials show a 2.2× throughput improvement over ALOHA (49% vs. 22% delivery ratio at 10 nodes/hop), 64% collision reduction, and 67% energy efficiency gains, demonstrating resilient emergency communications for underground environments. Full article

The growing demand for renewable energy in space-constrained environments highlights the need for compact, high-efficiency wind energy systems. Conventional bare wind turbine (BWT) arrays suffer from severe wake interactions and performance degradation when operated in tandem or closely spaced configurations. To address these limitations, this study investigates the aerodynamic performance and near-wake dynamics of a novel multi-ducted wind turbine (MDWT) system that integrates passive flow-control technique (PFCT) into an innovative fixed-duct design. The objective is to evaluate how tandem ducted arrangements with this integrated mechanism influence wake recovery, vortex dynamics, and power generation compared with multi-bare wind turbine (MBWT) systems. A numerical approach is employed using the Unsteady Reynolds-Averaged Navier–Stokes (URANS) formulation with the k–ω SST turbulence model, validated against experimental data. The analysis focuses on two identical, fixed-orientation ducts arranged in tandem without lateral offset, tested under three spacing configurations. The results reveal that the ducted system accelerates the near-wake flow and displaces velocity-deficit regions downward due to the passive flow-control sheets, producing stronger inflow fluctuations and enhanced turbulence mixing. These effects improve wake recovery and mitigate energy losses behind the first turbine. Quantitatively, the MDWT array achieves total power outputs 1.99, 1.90, and 1.81 times greater than those of the MBWT array for Configurations No. 1, No. 2, and No. 3, respectively. In particular, the second duct in Configuration No. 1 demonstrates a 3.46-fold increase in power coefficient compared with its bare counterpart. These substantial gains arise because the upstream duct–PFCT assembly generates a favorable pressure gradient that entrains ambient air into the wake, while coherent tip vortices and redirected shear flows enhance mixing and channel high-momentum fluid toward the downstream rotor plane. The consistent performance across spacings further confirms that duct-induced flow acceleration and organized vortex structures dominate over natural wake recovery effects, maintaining efficient energy transfer between turbines. The study concludes that closely spaced MDWT systems provide a compact and modular solution for maximizing energy extraction in constrained environments. Full article

Fractional calculus provides powerful tools for modeling nonlocality, dissipative systems, and, when defined in the time representation, provides an interesting memory effect in mathematical physics. In this paper, we review four standard fractional approaches: the Riemann–Liouville, Gerasimov–Caputo, Grünwald–Letnikov, and Riesz formulations. We present their definitions, basic properties, Weyl–Marchaud, and physical interpretations. We also give a brief review of related operators that have been used recently in applications but have received less attention in the physical literature: the fractional Laplacian, conformable derivatives, and the Fractional Action-Like Variational Approach (FALVA) for variational principles with fractional action weights. Our emphasis is on how these operators are, and can be, applied in physical problems rather than on exhaustive coverage of the field. This review is intended as an accessible introduction for physicists working in diverse areas interested in fractional calculus and fractional methods. For deeper technical or domain-specific treatments, readers are encouraged to consult the works in the corresponding fields, for which the bibliography suggests a starting point. Full article

Grid-forming wind-storage systems can serve as black-start power sources capable of autonomously establishing voltage and frequency references when the external grid is unavailable, thereby providing crucial support for rapid grid restoration. However, during the black-start process, energizing unloaded transformers often induces severe magnetizing inrush currents, which may cause transient overcurrent, damage grid-forming converters, and compromise system stability. To address this issue, this paper proposes a segmented zero-voltage start strategy and a dual-side converter multi-mode switching control scheme based on small-capacity distributed energy storage. First, the formation mechanism of transformer magnetizing inrush under no-load energization is analyzed. A segmented zero-voltage start module is embedded into the outer voltage loop of the virtual synchronous generator (VSG) controller to enable a smooth rise in output voltage, effectively mitigating transient impacts caused by magnetic core saturation. Second, considering the operating requirements during self-start and load restoration stages, a coordinated control framework for dual-side converters is designed to achieve dynamic voltage, frequency, and power regulation with limited energy storage capacity, thereby improving transient stability and energy utilization efficiency. Finally, real-time hardware-in-the-loop (HIL) simulations conducted on an RT-LAB platform verify the feasibility of the proposed control strategy. The results demonstrate that the method can significantly suppress magnetizing inrush current, transient overvoltage, and overcurrent, thus enhancing the success rate and dynamic stability of black-start operations in grid-forming wind-storage systems. Full article

This paper investigates the abnormal combustion behavior—specifically knock and pre-ignition—of a hydrogen direct-injection (H₂-DI) engine operated under stoichiometric conditions. Two different cylinder head configurations with central and lateral injector placement are analyzed using thermodynamic measurements, CFD simulations, and the optical diagnostic system VISIOLution^®. The results show that combustion stability and knock tendency are significantly influenced by injector positioning, injection pressure, and ignition timing. Controlled mixture formation and high turbulence during the compression phase are key to achieving both high power density and thermal efficiency in hydrogen-fueled engines. Full article

To enhance the operational efficiency of fuel cell engineering vehicles in transportation, reliable energy management strategies (EMSs) are essential for optimizing fuel consumption and power distribution. In this paper, we propose a novel energy management framework that utilizes a reinforcement learning-based adaptive hierarchical equivalent consumption minimization strategy (ECMS) to regulate fuel cell/battery hybrid system. The structure integrates deep Q-network (DQN), fuzzy logic, and ECMS algorithms and employs a long short-term memory neural network for working condition prediction. By combining DQN with the equivalence factor obtained using the battery state of charge penalty function and adjusting it using a fuzzy logic controller, the stability of the subsequent ECMS is enhanced. In a simulation environment, the proposed EMS achieves a 97.44% fuel economy compared to the dynamic programming-based global optimized EMS. Experimental findings indicate that the hierarchical ECMS effectively decreases the equivalent hydrogen consumption by 3.38%, 9.12%, and 16.39% compared to the adaptive ECMS, DQN-based ECMS, and classic ECMS, respectively. Therefore, the proposed methodology offers superior economic benefits. Full article

Power conversion efficiency (PCE) of single-junction perovskite solar cells (PSCs) has already soared from 3.8% to more than 26%. Their potential for application in tandem architecture with silicon and other established solar technologies has been deemed the future of low-cost solar technology. However, the commercialization of this technology is critically limited by instability under operational and environmental stress. The instability in the PSCs stems from both internal mechanisms including ion migration, defect formation, and electrode or charge transport layer (CTL)-induced degradation as well as external stressors such as moisture, oxygen, heat, and illumination. A complete understanding of both the internal and external stimuli-induced degradation and their mitigation strategies is essential for improving the device’s longevity. This review aims to provide a complete overview of the degradation mechanisms and steps taken to mitigate the degradation issues. The first half of the review discusses the degradation mechanism caused by internal degradation factors and provides strategies to mitigate them, while the second half focuses on the external stressors and the approaches developed by the perovskite community to overcome them. The commercialization of PSCs will depend on a holistic approach that simultaneously ensures both intrinsically as well as extrinsically stable devices. Full article

Efficient scheduling of virtual power plants (VPPs) is essential for the integration of distributed energy resources into modern power systems. This study presents a CUDA-accelerated Multiple-Chain Simulated Annealing (MC-SA) algorithm tailored for optimizing VPP scheduling. Traditional Simulated Annealing algorithms are inherently sequential, limiting their scalability for large-scale applications. The proposed MC-SA algorithm mitigates this limitation by executing multiple independent annealing chains concurrently, enhancing the exploration of the solution space and reducing the requisite number of sequential cooling iterations. The algorithm employs a dual-level parallelism strategy: at the prosumer level, individual energy producers and consumers are assessed in parallel; at the algorithmic level, multiple Simulated Annealing chains operate simultaneously. This architecture not only expedites computation but also improves solution accuracy. Experimental evaluations demonstrate that the CUDA-based MC-SA achieves substantial speedups—up to 10× compared to a single-chain baseline implementation while maintaining or enhancing solution quality. Our analysis reveals an empirical power-law relationship between parallel chains and required sequential iterations (iterations ∝ chains${}^{-0.88\pm 0.17}$), demonstrating that using 50 chains reduces the required number of sequential iterations by approximately 10× compared to single-chain SA while maintaining equivalent solution quality. The algorithm demonstrates scalable performance across VPP sizes from 250 to 1000 prosumers, with approximately 50 chains providing the optimal balance between solution quality and computational efficiency for practical applications. Full article

With the accelerating digital transformation of modern society, numerous data center (DC) agents are connected to the distribution power networks (DPNs) via microgrid and engaging in fierce market competition. To address the asymmetric operational risks faced by each data center agent, particularly those arising from market volatility and equipment failures, a novel cooperative game-theoretic approach is proposed in this paper. Firstly, a cooperative operation framework for the microgrid-integrated data centers (MDCs) system is established from two dimensions: joint task allocation across MDCs on the computing side and energy sharing among MDCs on the power side. Moreover, an optimal operating model for MDCs is established, which integrates the task allocation model that takes into account the task processing capacity of MDCs. Then, a cooperative operation model for the MDCs system based on Nash game theory is developed, and a joint solution framework for task allocation and the cooperative operation model is designed. Finally, the proposed cooperative game-theoretic approach is validated in a test system. The results show that the proposed approach ensures the reliable operation of the DPN while avoiding asymmetric operation risks among MDCs. It enhances the stability and security of distributed data processing. Furthermore, the Nash game-theoretic model achieves a symmetric distribution of profits and risks across MDCs, eliminating individual biases and maximizing the overall benefits of the cooperative alliance. Full article

The integration of modern artificial intelligence into software systems presents transformative opportunities and novel challenges for software quality assurance (SQA). While AI enables powerful enhancements in testing, monitoring, and defect prediction, it also introduces non-determinism, continuous learning, and opaque behavior that challenge traditional quality and reliability paradigms. This paper proposes a framework for addressing these issues, drawing on concepts from systems theory. We argue that AI-enabled software systems should be understood as dynamical systems, i.e., stateful adaptive systems whose behavior depends on prior inputs, feedback, and environmental interaction, as well as components embedded within broader socio-technical ecosystems. From this perspective, quality assurance becomes a matter of maintaining stability by enforcing constraints as well as designing robust feedback and control mechanisms that account for interactions across the full ecosystem of stakeholders, infrastructure, and operational environments. This paper outlines how the systems-theoretic perspective can inform the development of modern SQA processes. This ecosystem-aware approach repositions software quality as an ongoing, systemic responsibility, especially important in mission-critical AI applications. Full article

This study explores the transformative role of Cyber-Physical Power System (CPPS) Digital Twins (DTs) in enhancing the operational resilience, flexibility, and intelligence of modern power grids. By integrating physical system models with real-time cyber elements, CPPS DTs provide a synchronized framework for real-time monitoring, predictive maintenance, energy management, and cybersecurity. A structured literature review was conducted using the ProKnow-C methodology, yielding a curated portfolio of 74 publications from 2017 to 2025. This corpus was analyzed to identify key application areas, enabling technologies, simulation methods, and conceptual maturity levels of CPPS DTs. The study highlights seven primary application domains, including real-time decision support and cybersecurity, while emphasizing essential enablers such as data acquisition systems, cloud/edge computing, and advanced simulation techniques like co-simulation and hardware-in-the-loop testing. Despite significant academic interest, real-world implementations remain limited due to interoperability and integration challenges. The paper identifies gaps in standard definitions, maturity models, and simulation frameworks, underscoring the need for scalable, secure, and interoperable architectures and highlighting key areas for scientific development and real-life application of CPPS DTs, such as grid predictive maintenance, forecasting, fault handling, and power system cybersecurity. Full article