Search Results (909)

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of $R^2=0.697$ and $R^2=0.794$ for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops. Full article

Carbon-fibre reinforced polymer (CFRP) laminates are susceptible to both static and fatigue-driven delamination. Predicting this type of failure in curved composite structures, often referred to as delamination by unfolding, remains a critical challenge. This work presents the development of a Virtual Crack Closure Technique (VCCT)-based computational method for simulating fatigue-driven delamination propagation under non-constant mixed-mode conditions. The fatigue delamination growth model follows a phenomenological approach based on a Paris–Erdogan-based power-law relationship, where the delamination propagation rate depends on the strain energy release rate. This methodology has been implemented as a user-defined subroutine, UMIXMODEFATIGUE, for Abaqus, integrating the effects of load ratio and mode mixity conditions while leveraging the mode separation provided by VCCT. The proposed approach is validated against an experimental case involving a four-point bending test applied to an L-shaped CFRP curved beam specimen with a unidirectional layup. Unlike the existing standard configuration, the proposed test campaign introduces a non-adhesive Teflon foil insert at the bend, placed within the midplane layers to act as a delamination initiator, representing a manufacturing defect. In addition to the testing machine, digital image correlation (DIC) is used to monitor delamination length. The simulation method developed accurately predicts fatigue delamination propagation under varying mode mixity at the delamination front. By improving delamination modelling in composites, this approach supports timely maintenance and helps prevent fatigue failures. Additionally, it deepens the understanding of how the mode mixity influences the delamination propagation process. Full article

With the large-scale integration of new energy and power electronic devices into power systems, frequency stability has become an increasingly critical concern. To maintain frequency stability while mitigating the high capital expenditure of energy storage systems (ESSs), this paper develops a control framework centered on edge energy management terminals (EEMTs). The design is based on a demonstration project in which distributed energy resources (DERs) and flexible loads collaboratively provide frequency regulation. A monitoring station is implemented to make fast frequency response (FFR) resources dispatchable, detectable, measurable, and tradable. Furthermore, a control strategy tailored for building- and factory-level applications is proposed. This strategy enables real-time optimal scheduling of DERs and flexible loads through coordinated communication between EEMTs and net load units (NLUs). Two field tests further demonstrate the effectiveness and advantages of the proposed approach. In addition, this paper proposes a coordinated scheme in which wind farms and NLUs jointly participate in frequency regulation, aiming to mitigate the response delay of NLUs and the secondary frequency drop observed in wind farms. The feasibility and benefits of this scheme are validated through experimental tests. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article

To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balance between load shifting, load management and power dispatch. In this regard, this research study aims to investigate the efficiency of various machine learning models for whole-house energy consumption prediction and appliance-level load disaggregation using Non-Intrusive Load Monitoring (NILM). The primary objective is to determine which model offers the most accurate forecasts for both individual appliance consumption patterns and the total amount of energy used by the household. The empirical study presents comparative performance analysis of machine learning models, i.e., Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting and Support Vector Regressor (SVR) for load forecasting and load disaggregation. This research is conducted on PRECON: Pakistan Residential Electricity Dataset consisting of 42 Pakistani households. The dataset was recorded originally as one minute per sample, but the proposed study aggregated it to hourly samples to evaluate models’ alignment with the typical sampling rate of smart meters in Pakistan. It enables the models to more accurately depict implementation scenarios in real-world settings. The statistical measures MAE, MSE, RMSE and R² have been employed for performance evaluation. The proposed Random Forest algorithm out-performs all other employed models, with the lowest error values (MAE: 0.1316, MSE: 0.0367, RMSE: 0.1916) and the highest R² score of 0.9865. Furthermore, for detecting appliance events from aggregate power data, ensemble models such as Random Forest performed better than other models for ON/OFF prediction. To evaluate the suitability of machine learning models for real-time, appliance-level energy forecasting using Non-Intrusive Load Monitoring (NILM), this study presents a novel evaluation framework that combines learning speed and edge adaptability with conventional performance metrics (e.g., R², MAE). This paper introduces a NILM-based approach for load forecasting and appliance-level ON/OFF prediction, representing its capacity to improve residential energy efficiency and encourage sustainable energy consumption, while emphasizing operational metrics for implementation in embedded smart grid systems—an area mainly neglected in prior NILM-based research articles. The results provide useful information for improving demand-side energy management, facilitating more effective load disaggregation, and maximizing the energy efficiency and responsiveness of smart grids. Full article

In intermittent sports, tracking technologies are commonly used to monitor external and internal loads. The metabolic power model solely uses speed and acceleration data to simulate metabolic power, oxygen uptake, energy expenditure, and aerobic–anaerobic supplies. This study aimed to improve the simulation of oxygen uptake within the metabolic power model, thereby increasing its validity to estimate metabolic loads during intermittent running. Twelve male athletes (24 ± 3 years) performed different intermittent running-based exercises. These data were previously collected and used for secondary analysis within this study. The simulation of oxygen uptake was optimized by different approaches: (i) formerly detected bias (Offset model), (ii) data-driven modeling using differential evolution (Mongin model), and (iii) correction of the aerobic supply calculation. The simulations were compared to the measured oxygen uptake via a portable respiratory gas analyzer and the resulting metabolic loads to those derived by the established 3-component model. For statistical analysis, one-way repeated measures ANOVA or Friedman test with corresponding effect sizes were applied. Overall, the Mongin model demonstrated the best predictive accuracy (MAE = 4.99 ± 1.12 mL/min/kg) compared to measured oxygen uptake and, combined with the corrected calculation, total energy expenditure and aerobic supply did not significantly differ to the standard (p ≥ 0.056; trivial to large effect sizes). In conclusion, our optimizations reduce discrepancies of the tracking-based metabolic power model regarding total energy expenditure and aerobic supply compared to the established 3-component model. Full article

Modern power grids face increasing stress from volatile, high-dynamics loads, such as Electric Vehicle (EV) charging clusters and intermittent renewable energy sources. Accurate transformer thermal monitoring via the International Electrotechnical Commission (IEC) 60076-7 and the Institute of Electrical and Electronics Engineers (IEEE) C57.91 standards is crucial, yet their methodologies differ significantly. This study develops a comprehensive MATLAB simulation framework to quantify these differences. The analysis compares physical thermal models across multi-stage cooling—Oil Natural Air Natural (ONAN), Oil Natural Air Forced (ONAF), and Oil Forced Air Forced (OFAF)—and insulation aging models. It is demonstrated that divergence in transformer life estimation stems primarily from the physical thermal models. A ‘reversal of conservatism’ is identified, where ‘conservative’ is defined as predicting higher hot-spot temperatures and enforcing a larger safety margin. Results prove that while the IEC model is thermally more conservative during cooling failures (static mode), the IEEE model is consistently more conservative during normal active cooling. Additionally, 2D “heat maps” are presented to define safe operational zones, and the catastrophic impact of cooling system failures is quantified. These findings provide a quantitative outline for managing transformer state under increasingly demanding loading schemes. Full article

This paper presents a portable cluster architecture based on a lightweight Kubernetes distribution designed to provide enhanced computing capabilities in isolated environments. The architecture is validated in two operational scenarios: (1) machine learning operations (MLOps) for on-site learning, fine-tuning and retraining of models and (2) web hosting for isolated or resource-constrained networks, providing resilient service delivery through failover and load balancing. The cluster leverages low-cost Raspberry Pi 4B units and virtualized nodes, integrated with Docker containerization, Kubernetes orchestration, and Kubeflow-based workflow optimization. System monitoring with Prometheus and Grafana offers continuous visibility into node health, workload distribution, and resource usage, supporting early detection of operational issues within the cluster. The results show that the proposed dual-mode cluster can function as a compact, field-deployable micro-datacenter, enabling both real-time Artificial Intelligence (AI) operations and resilient web service delivery in field environments where autonomy and reliability are critical. In addition to performance and availability measurements, power consumption, scalability bottlenecks, and basic security aspects were analyzed to assess the feasibility of such a platform under constrained conditions. Limitations are discussed, and future work includes scaling the cluster, evaluating GPU/TPU-enabled nodes, and conducting field tests in realistic tactical environments. Full article

Occupational demands, such as load carriage in tactical professions, do not discriminate based on sex. The aim of this study was to explore the differences in metabolic cost of a loaded pack march between the sexes in both absolute and relative terms. Twelve Army personnel (six males and six females) volunteered to complete three identical load carriage marches (5 km at 5.5 km/h, carrying 30 kg), across flat (on road) and undulating (gravelled path) terrain as part of a larger equipment trial. Heart rate (HR) response (HR average and maximum) was monitored with a Polar Team Pro unit and oxygen consumption with VO Master Pro (VO₂ average and maximum) with the level of significance set at 0.05. There were no significant differences in age, years of experience, absolute loads carried, or completion time for each of the three events. Male soldiers were significantly taller (182.3 ± 6.2 cm vs. 167.4 ± 6.9 cm), heavier (88.2 ± 8.7 kg vs. 70.9 ± 10.6 kg), carried significantly less relative load (34.3 ± 3.4% vs. 43.2 ± 7.5%), and had significantly greater predicted VO_2max (56.7 ± 6.1 mL/kg/min vs. 45.0 ± 2.9 mL/kg/min). A linear mixed model identified a significant main effect of sex on both average HR (β = −1.10) and peak HR (β = −1.27), and on average VO₂ (β = −0.68), but not peak VO₂. While the study was not powered to detect sex differences, the large effect sizes observed suggest meaningful physiological differences warranting further investigation. Female soldiers faced significantly greater metabolic costs when carrying the same loads and moving at the same speed and across the same terrain as their male counterparts. Adequate recovery and pacing strategies should be considered for these events, especially during training. Full article

This paper presents the characterization of a new optical sensor designed for monitoring overhead power lines (OHLs) by determining key mechanical parameters of electrical conductors. The device employs fiber Bragg gratings (FBGs) written into a metal-coated fiber and enclosed within a Kovar^® capillary tube. Its epoxy-free design provides robust hermetic protection for the FBGs, enabling reliable performance with both conventional low-temperature and high-temperature low-sag (HTLS) conductors. The sensor configuration enables direct measurements of conductor strain and temperature, as well as indirect estimation of sag and related mechanical quantities such as tension and stress. Laboratory tests were carried out over a temperature range of 30 °C to 200 °C and for applied forces up to 2 kN. The experimentally determined sensitivities were about 0.4 nm/kN for force and 27 pm/°C for temperature. The device endured ten successive thermal cycles between 30 °C and 200 °C, maintaining its force sensitivity within 20% variation throughout the tests. These results confirm that the developed sensor can simultaneously track temperature and mechanical load across the investigated temperature range, demonstrating its potential for HTLS conductor monitoring in power transmission networks. Full article

Permanent Magnet Synchronous Generators (PMSGs) have become increasingly important in industrial applications such as wind turbine systems due to their high efficiency and power density. However, their operational reliability can be affected by asymmetries such as static eccentricity (SE) and load current unbalance (UnB), which exhibit similar spectral features and are therefore difficult to differentiate using conventional techniques such as Motor Current Signature Analysis (MCSA). Stray flux analysis provides an alternative diagnostic approach, yet single-point measurements often lack the sensitivity required for accurate fault discrimination. This study introduces a diagnostic methodology based on the Space Vector Stray Flux (SVSF) for identifying static eccentricity (SE) and load current unbalance (UnB) faults in PMSG-based systems. The SVSF is derived from three external stray flux sensors placed 120° electrical degrees apart and analyzed through symmetrical component decomposition, focusing on the +5f_s positive-sequence harmonic. Two-dimensional Finite Element Analysis (FEA) conducted on a 36-slot/12-pole PMSG model shows that the amplitude of the +5f_s harmonic increases markedly under static eccentricity, while it remains nearly unchanged under load current unbalance. To validate the simulation findings, comprehensive experiments have been conducted on a dedicated test rig equipped with high-sensitivity fluxgate sensors. The experimental results confirm the robustness of the proposed SVSF method against practical constraints such as sensor placement asymmetry, 3D axial flux effects, and electromagnetic interference (EMI). The identified harmonic thus serves as a distinct and reliable indicator for differentiating static eccentricity from load current unbalance faults. The proposed SVSF-based approach significantly enhances the accuracy and robustness of fault detection and provides a practical tool for condition monitoring in PMSG. Full article

The main objective of this study was to develop a comprehensive testing method for powered roof supports operating under real mining conditions and to establish guidelines for a monitoring system designed to record their geometric and operational parameters. The proposed methodology included analyses of load-bearing capacity limits, laboratory model tests, bench tests, and in situ investigations under actual working conditions. Based on these studies, a detailed testing procedure was developed, defining the sequence of experimental stages, the selection and calibration of sensors, their installation and servicing methods, as well as the integration of measuring equipment with the support structure. The key results demonstrate that the proposed method allows for reliable acquisition and interpretation of data concerning the operational behavior of powered roof supports. The findings enabled the identification of critical geometric and operational parameters influencing the stability, durability, and efficiency of the support system. The developed monitoring procedure, supported by both laboratory and field tests, provides a consistent and replicable framework for assessing the performance of roof supports in real-time mining operations. The conclusions confirm that the presented approach represents an innovative and systematic method for evaluating and monitoring powered roof supports under real conditions. The main contribution of this work lies in the formulation of universal guidelines for the design and implementation of monitoring systems, significantly improving the safety, reliability, and efficiency of mining processes. Full article

Structures with slender cylindrical geometries, such as subsea power cables are critical components of infrastructure systems. These structures are prone to bending deformation under load, which can ultimately cause structural failure. In this study, distributed optical fibre sensors are used to monitor the bending deformation in slender cylindrical structures. Brillouin optical time-domain reflectometry-based strain sensing was used to experimentally study three-point bending and approximately constant curvature bending of a 6 m long circular hollow section (CHS). Optical fibres were attached to the outer surface of the CHS in two different configurations: parallel to the longitudinal axis and helically wound around the CHS. Strain responses due to changing magnitudes of deformation and changing orientation of the optical fibre around the circumference of the CHS were studied. A finite element model was employed to simulate and interpret the observed strain responses. A strain response inverse analysis was conducted using the strain data obtained from the experimental study to reconstruct the deformed shapes of the CHS. Both the longitudinally aligned and helically wound fibres showed distinct strain profiles that differentiate the three-point bending and constant curvature bending behaviours. The results revealed the ability of optical fibre sensing to evaluate the type; magnitude; and orientation of the bending deformations. This fundamental understanding supports the design of sensing systems for critical cylindrical infrastructure. Full article

The proliferation of power converters in modern energy production systems has led to increased harmonic content due to the commutation of active switching devices. This increase in harmonics contributes to lower system efficiency, reduced power factor, and consequently, a higher reactive power requirement. To address these issues, this paper presents both simulation and experimental results of various control strategies implemented on Parallel Voltage Source Inverters (PVSI) for harmonic mitigation. The proposed control strategies are categorized into direct and indirect control methods. The direct control techniques implemented include the instantaneous power method (PQ), the synchronous algorithm (DQ), the maximum principle method (MAX), the algorithm based on synchronization of current with the voltage positive-sequence component (SEC-POZ), and two methods employing the separating polluting components approach using a band-stop filter and a low-pass filter. The main innovation in these active power filter (APF) control strategies, compared to traditional or existing technologies, is the real-time digital implementation on high-speed platforms, specifically FPGAs. Unlike slower microcontroller-based systems with limited processing capabilities, FPGA-based implementations allow parallel processing and high-speed computation, enabling the execution of complex control algorithms with minimal latency. Additionally, the enhanced reference current generation achieved through the seven applied methods provides precise harmonic compensation under highly distorted and nonlinear load conditions. Another key advancement is the integration with Smart Grid functionalities, allowing IoT connectivity and remote diagnostics, which enhances system monitoring and operational flexibility. Following validation on an experimental test bench, these algorithms were implemented and tested on industrial APF prototypes powered by a standardized three-phase network supply. All control strategies demonstrated an effective reduction in total harmonic distortion (THD) and improvement in power factor. Experimental findings were used to provide recommendations for choosing the most effective control solution, focusing on minimizing THD and enhancing system performance. Full article