Search Results (868)

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Industrial-scale Anaerobic Digestion (AD) is a key technology for the energy recovery of agro-industrial and municipal waste and for the mitigation of greenhouse gas emissions; however, the actual operational performance of industrial biodigesters continues to show significant discrepancies with respect to the theoretical values reported in the scientific literature. In this context, there is still a lack of systematic analysis to identify which operating parameters are consistently monitored in industrial settings and which remain insufficiently explored, particularly those that describe the overall state of the digestion environment. To address this gap, a systematic literature review was conducted in the Scopus database for the period 2000–2026, complemented by a bibliometric analysis using VOSviewer software v1.6.18. 3. After applying inclusion criteria focused exclusively on industrial-scale and pilot systems, 1327 documents corresponding to the category of operating parameters were selected and analyzed using keyword co-occurrence networks and evaluation of occurrence frequencies and total link intensities. The analysis shows a marked concentration of the literature on a small set of classic parameters, highlighting pH (154 occurrences, 3667 link intensities), temperature (147 occurrences, 3255 link intensities), and ammonia (131 occurrences, 2824 link intensities) as the most recurrent variables in the industrial operation of anaerobic digesters. Complementarily, parameters such as chemical oxygen demand, total and volatile solids, and hydrogen sulfide have progressively increased their presence since 2015, mainly associated with effluent quality assessment, nutrient recovery, and overall process sustainability. In contrast, variables that integrate the state of the environment, such as electrical conductivity, oxidation-reduction potential, and the rheological properties of digestate, appear in less than $5\%$ of the studies analyzed, despite their ability to integrate information on stability, buffer capacity, and overall operating conditions. Taken together, these findings highlight an imbalance between the intensive use of traditional parameters and the limited incorporation of integrative indicators in industrial monitoring, suggesting that their systematic inclusion, together with the development of soft sensors and predictive models, could contribute to improving operational control and reducing the gap between the theoretical performance and actual behavior of industrial biodigesters. Full article

Rapid and real-time monitoring of blood glucose concentration is critical for the diagnosis and management of diabetes, while conventional invasive detection methods suffer from inconvenience and discomfort, making non-invasive detection a research hotspot. In this study, a dual complementary split-ring resonator (DS-CSRR) operating at 3.3 GHz was designed and fabricated for non-invasive glucose concentration detection, aiming to address the problems of low sensitivity and large size of existing microwave glucose sensors. The sensor was fabricated on a low-cost FR4 dielectric substrate with dimensions of 20 × 30 × 0.8 mm³, and two U-shaped slots were incorporated into the traditional DS-CSRR structure to realize cross-polarization excitation. This design not only enhances the interaction between the electric field and glucose solution but also optimizes the quality factor (Q) and electric field distribution of the resonator without changing the overall size. Compared with the traditional DS-CSRR, the Q factor of the modified structure is increased to 130 under no-load conditions. The transmission coefficient Signal Port 2 to Port 1 (S21) of the sensor loaded with glucose solutions of different concentrations was measured using a vector network analyzer (VNA). The experimental results show a good linear frequency shift with the increase in glucose concentration, with a measured sensitivity of 1.95 kHz/(mg·dL⁻¹). In addition, the sensor is characterized by miniaturization, low cost and easy fabrication due to the adoption of standard PCB fabrication processes. This study successfully demonstrates a non-invasive microwave sensor with high sensitivity for glucose concentration detection, which has promising application potential in personal continuous glucose monitoring, and also provides a useful design strategy for the development of miniaturized high-sensitivity microwave biosensors. Full article

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of ${1.77}^{{°}^2}$, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs. Full article

Developing advanced functional materials requires the synergistic integration of nanoscale reinforcements with tailored properties. In this work, composite films of cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and reduced graphene oxide (rGO) were synthesized using a combination of solution casting, high shear homogenization, vacuum filtration, and environmentally friendly chemical reduction. The resulting CNC/CNF/rGO films exhibited a robust hierarchical structure with strong interfacial interactions, enabling exceptional mechanical properties, specifically a tensile strength of 215 MPa and a Young’s modulus of 18 GPa, alongside a continuous conductive network confirmed by frequency-independent electrical conductivity up to 30 kHz. Comprehensive dielectric characterization revealed frequency-dependent permittivity and low dielectric loss, aligning with Maxwell–Wagner theoretical predictions for heterogeneous composites. The composites also demonstrated thermal stability, with electrical conductivity increasing monotonically from 0 °C to 200 °C. These findings highlighted the CNC/CNF/rGO films’ suitability for applications in flexible electronics, electromagnetic shielding, packaging, and high-performance structural materials. Future optimization and modeling approaches, including fractional calculus, are recommended to further enhance multifunctionality and exploit the unique synergistic interactions intrinsic to nanocellulose–graphene oxide platforms. Full article

We present frequency- and magnetic field-dependent measurements of the complex dielectric permittivity ε*(f, H) of a kerosene-based ferrofluid, containing Mn₀.₆Fe₀.₄Fe₂O₄ nanoparticles, over 0.8–5 GHz and static fields up to ~91 kA/m. The imaginary part, ${\epsilon }_F^{″}$, shows a peak at a characteristic frequency that shifts towards higher frequencies with increasing H, revealing a magnetic field-dependent relaxation process, interpreted using the Maxwell–Wagner–Sillars model. The dielectrophoretic extraction of nanoparticles was evaluated via the squared electric field gradient, and a threshold, ${\left(\nabla E^2\right)}_{\min }$, dependent on particle size was determined. Below that threshold, Brownian forces dominate, so the ferrofluid acts as a homogeneous dielectric. For this case, the Clausius–Mossotti factor (CM) was calculated for ferrofluid droplets in air and in water as a function of frequency and magnetic field. In air, CM exhibits modest but systematic magnetic field dependence, indicating a magnetically modulated dielectric response at GHz frequencies. In contrast, when water is used as the reference medium, CM remains negative and essentially independent of H across the entire frequency range, suggesting that the high permittivity of water masks the magneto-dielectric effects in the ferrofluid. These findings provide insight into the interplay between the magnetic field and the permittivity of ferrofluids, with implications for high-frequency applications. Moreover, using a λ/4 antenna connected to a network analyzer, the existence of the dielectrophoretic force acting on a ferrofluid-impregnated textile thread at microwave frequencies was experimentally demonstrated. Full article

Lightning-induced breaking accidents of medium-voltage insulated conductors pose a serious threat to the safety of distribution networks, and the key cause lies in the establishment and sustained combustion of the power-frequency follow-current arc after lightning overvoltage breakdown. This paper systematically investigates the formation mechanism and critical conditions of power-frequency follow-current arcs using combined simulation and experimental approaches. Based on the streamer discharge theory, a lightning breakdown model was established and combined with the arc energy balance equation, revealing that the establishment of power-frequency follow-current arcs is essentially determined by the post-breakdown energy competition process. The simulation results show that the required anode electric field strength for lightning breakdown is not less than 3 kV/mm. When the power-frequency voltage reaches 10 kV, Joule heating of the arc continuously exceeds heat dissipation loss, enabling restrike after zero-crossing and sustaining stable burning. Experiments verified this voltage threshold and further revealed that the arc establishment rate exhibits nonlinear growth with increasing power-frequency voltage, exceeding 90% at power-frequency voltages ≥ 10 kV. The study also reveals that increased gap distance reduces the arc establishment rate, while the introduction of insulators can enhance it by approximately 20%. This study clarifies the energy criterion for power-frequency follow-current arc establishment and the influence patterns of key parameters, providing theoretical basis and engineering reference for lightning protection design and arc suppression in medium-voltage insulated lines. Full article

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms. Full article

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions. Full article

The rapid growth of electric vehicles (EVs) is reshaping transport systems and accelerating the sustainable digital transformation of smart mobility. EV battery-swapping, delivered through platform-based, data-driven service networks, offers a low-carbon alternative to conventional refueling and plug-in charging by shortening replenishment time and enabling centralized battery management. However, the behavioral mechanisms driving user adoption of this digitally enabled infrastructure remain insufficiently understood. This study develops a socio-technical system (STS) model in which social and technical drivers influence users’ intention to adopt EV battery-swapping services via the dual mediation of perceived trust and perceived risk. Using a three-stage mixed-methods design that combines a PRISMA-based literature review, expert interviews with user-journey mapping, and a large-scale user survey, the study identifies six social and technical antecedents of EV battery-swapping adoption. Based on 565 valid responses from EV users in the Beijing–Tianjin–Hebei region, partial least squares structural equation modeling and multi-group analysis are employed to test the proposed framework. The results show that all six antecedents significantly affect perceived trust and perceived risk, which in turn mediate their impacts on adoption intention, with notable heterogeneity across income and usage-frequency groups. The findings provide a mechanism-based extension of STS theory for digitally mediated battery-swapping infrastructure by showing how socio-technical conditions shape adoption via trust and risk, and they offer actionable implications for operators and policymakers to build secure, user-centered swapping services within intelligent transport systems. Full article

Against the backdrop of increasingly scarce global arable land resources, the remediation and resource utilization of saline–alkali soils have become a critical issue in circular agriculture. This study proposes micro-nano bubble (MNB) irrigation technology as a green, low-carbon strategy for saline–alkali soil remediation, highlighting its multi-level driving mechanism through pot experiments at different aeration frequencies. Results indicated that MNB irrigation significantly enhanced salt leaching and acid-base neutralization by reducing the soil pH (11.75%) and electrical conductivity (53.41%). Meanwhile, soil organic matter, cation exchange capacity, and available nitrogen, phosphorus, and potassium increased to normal soil levels. MNBs also strongly activated native enzymes (urease and alkaline phosphatase), raising the total enzyme activity by 68.54%, which is linked to carbon, nitrogen, and phosphorus metabolism. These results were also validated by microbial analysis, which indicated that MNBs shifted the community structure from one dominated by salt-tolerant taxa (i.e., Pseudomonadota) to a more functionally beneficial composition (i.e., Bacillota). Through these changes, the microbial diversity and network connectivity were enhanced, with Qipengyuania and Psychrophilus identified as critical nodes. This study reveals the multi-level driving mechanism of MNB technology, providing new technical pathways and theoretical support for the remediation, resource recovery, and circular utilization of agricultural waste soils. Full article

Stochastic Subspace Identification (SSI) theory offers the distinct advantage of extracting modal parameters directly from operational ambient excitations without requiring artificial force, ensuring completely true boundary conditions and providing extensive field measurement data. In this study, we systematically investigate the operational modal characteristics of Electric Multiple Units (EMUs) in the Chinese high-speed railway network under multi-dimensional coupling conditions, including wide speed ranges, axle load perturbations, air spring faults, and coupled operation. The results reveal that while car body modal frequencies remain largely insensitive to operating speed—indicating negligible effects of aerodynamic stiffness—they exhibit distinct sensitivities to mass and boundary variations. Specifically, an increase in axle load induces a significant attenuation (exceeding 5%) in low-order vertical bending frequencies, conforming to the dynamic mass law. Conversely, air spring deflation triggers a sharp increase in boundary stiffness, resulting in a 13.6% surge in torsional modal frequency, which serves as a critical indicator for fault diagnosis. Furthermore, coupled operation is found to primarily enhance system damping. Based on these findings, we establish a “condition-modal” vehicle sensitivity matrix, quantifying dynamic evolution mechanisms under complex boundaries and providing a vital baseline for monitoring the structural health of railway vehicles and conducting intelligent maintenance. Full article

This study investigates the ongoing electrical activity of local neural networks—referred to as neurodynamics—across 37 anatomically defined brain regions. We analyzed stereotactic intracranial EEG (sEEG) recordings from 106 subjects during wakeful rest, focusing on scale-free (power-law) properties to determine whether distinct brain regions exhibit unique neurodynamic signatures. Results revealed a power-law regime in two frequency ranges (approximately 0.5–4 Hz and 33–80 Hz). Notably, the power-law exponent (slope) in the high-frequency band differed significantly between cortical and subcortical areas (p < 0.01). These findings suggest that local neurodynamics, as reflected in scale-free characteristics, may serve as a functional “fingerprint” for brain region classification. This approach may contribute to functional brain parcellation efforts and offer new insights into the intrinsic organization of neuronal networks as revealed by resting-state activity analysis. Full article

The frequency of extreme weather events has become higher, and electricity consumption has also become more complex. These changes increase the risk of overload in distribution transformers (DTs), and this risk threatens the stability and reliability of the power grid. Existing methods have significant limitations. Traditional static threshold methods (based on DGA gas ratios and electrical signal thresholds) fail to consider temporal changes and complex links between factors, while modern machine learning models lack cause–effect relationships over time and clear ways to describe uncertainty. With such motivations, this paper proposes a causal-enhanced hybrid framework, which combines Long Short-Term Memory (LSTM) networks and Random Forest (RF) algorithms. The framework uses causal Seasonal Trend decomposition using Loess (STL) to reveal load patterns at different time scales. The mutual information index and spatiotemporal graph convolutional network (ST-GCN) are used to explore nonlinear relations and reveal how temperature affects load changes. The LSTM model captures time dependence in load series, and the Bayesian optimized Random Forest is used to solve the problem of data imbalance and quantify uncertainty. In addition, the framework constructs an early warning system that combines data from many sources in real time. Test results show that the proposed algorithm exhibits excellent performance in multi-source data environments. Full article

The challenges of ensuring the security of electricity supply (SoES) in large aluminium smelters—particularly those that are self-supplied—provide a compelling rationale for further investigation, as research on this class of industrial systems is limited. Firstly, this paper presents an expert technical perspective on the distinct characteristics and operational challenges associated with aluminium potline loads and their supply systems in self-supplied aluminium smelters. This study then examines the supply infrastructure at Emirates Global Aluminium’s plant in Dubai, which has an installed power generation capacity of 3000 MW, supplying a 2000 MW load on a continuous basis through a network of three 132 kV substations. This high-voltage network is modelled and simulated using the CYME network analysis software module. We consider the following key approaches to ensure stable system voltage for desirable SoES: steady-state voltage control, outage planning and reactive power reserve management, active power flow management and load participation. We then study the influence each of these has on the system voltage and, hence, on the overall SoES of the smelter, using time-domain voltage and frequency curves at key network nodes and active power flow through important network interconnectors. The simulation results clearly demonstrate a significant improvement in the base case event by positively damping the oscillations in these responses, highlighting the significance of maintaining a healthy system voltage within a limit of ±2% of the nominal voltage to ensure SoES of the smelter. Full article