Search Results (656)

When a transformer operates under three-phase unbalanced conditions, the location of the winding hot-spot temperature (HST) is no longer fixed on a certain phase. Taking an S13-M-100 kVA/10 kV transformer as the research object, this paper proposes a streamline inversion method for inverting the winding HST based on the analysis of oil flow morphology. The study employs the finite volume method for coupled calculations of a transformer’s thermal fluid field and combines a support vector regression (SVR) model for the HST inversion. An orthogonal experimental method is used to construct the training and testing sample sets, and the grid search method is utilized to optimize the parameters of the SVR model. In response to variations in hot-spot locations under three-phase unbalanced conditions, representative streamlines are reasonably selected, and a genetic algorithm-based dimensionality reduction optimization is performed on the feature quantities. The research results indicate that the established inversion model exhibits high inversion accuracy under three-phase unbalanced conditions, with a maximum temperature difference of 3.71 K, and the robustness check verifies the stability of the model. Full article

This study explores practical approaches to improving the reliability of power supply systems through the expansion and optimization of substation power lines. As electricity demand steadily increases, ensuring a stable and efficient power delivery network has become essential to support industrial growth and socio-economic development. This study focuses on challenges such as vulnerability to single-line faults, limited transmission capacity, and complex coordination in system operation. To address these issues, the proposed strategy includes building redundant transmission lines, improving network configuration, and applying modern transmission technologies to enhance operational flexibility. Notably, a Deep Q-Learning algorithm is introduced during the planning and optimization process. Its ability to accelerate convergence and streamline decision making significantly reduces computation time while maintaining solution accuracy, thereby increasing overall efficiency in evaluating large-scale network configurations. Simulation results and case studies confirm that such improvements lead to shorter outage durations, enhanced fault tolerance, and better adaptability to future load demands. The findings highlight strong practical value for industrial applications, offering a scalable and cost-conscious solution for strengthening the reliability of modern power systems. Full article

Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a Bayesian GAT method specifically tailored for vibration-based compressor fault diagnosis. The approach integrates domain-specific digital-twin simulations built with Rotordynamic software (1.3.0), and constructs dual adjacency matrices to encode both physically informed and data-driven sensor relationships. Additionally, a hybrid forecasting-and-reconstruction objective enables the model to capture short-term deviations as well as long-term waveform fidelity. Monte Carlo dropout further decomposes prediction uncertainty into aleatoric and epistemic components, providing a more robust and interpretable model. Comparative evaluations against conventional Long Short-Term Memory (LSTM)-based autoencoder and forecasting methods demonstrate that the proposed framework achieves superior fault-detection performance across multiple fault types, including misalignment, bearing failure, and unbalance. Moreover, uncertainty analyses confirm that fault severity correlates with increasing levels of both aleatoric and epistemic uncertainty, reflecting heightened noise and reduced model confidence under more severe conditions. By enhancing GAT fundamentals with a domain-tailored dual-graph strategy, specialized Bayesian inference, and digital-twin data generation, this research delivers a comprehensive and interpretable solution for compressor fault diagnosis, paving the way for more reliable and risk-aware predictive maintenance in complex rotating machinery. Full article

Australia has the highest per capita penetration of rooftop solar PV systems in the world. Integration of these systems has led to reverse power flow and associated voltage rise problems in residential low-voltage (LV) distribution networks. Furthermore, random, uncontrolled connection of single-phase solar systems can exacerbate voltage unbalance in these networks. This paper investigates the application of a Static Synchronous Compensator (STATCOM) for the improvement of voltage regulation in four-wire LV distribution feeders through reactive power management as a means of mitigating voltage regulation and unbalance challenges. To demonstrate the performance of the STATCOM with varying loads and PV output, a Q-V droop curve is applied to specify the level of reactive power injection/absorption required to maintain appropriate voltage regulation. A practical four-wire feeder from New South Wales, Australia, has been used as a case study network to analyse improvements in system performance through the use of the STATCOM. The outcomes indicate that the STATCOM has a high degree of efficacy in mitigating voltage regulation and unbalance excursions. In addition, compared to other solutions identified in the existing literature, the STATCOM-based solution requires no sophisticated communication infrastructure. Full article

About one billion people worldwide are affected by neurologic disorders. Among the various neurologic disorders, one of the most common is Alzheimer’s disease (AD). AD is a neurodegenerative disorder that progressively affects cognitive functions, disrupting the daily lives of millions of individuals. Mild cognitive impairment (MCI) is often considered a prodromal stage of Alzheimer’s disease. In this article, we retrieved data from the online available dataset GSE63060, which includes transcriptomic data of 329 blood samples, of which there are 104 cognitively normal controls, 80 MCI patients, and 145 AD patients. We used transcriptomic data related to all three groups to perform an over-representation analysis of the gene ontologies followed by a network analysis. The aim of our study is to pinpoint alterations, detectable through a non-invasive method, in biological processes affected in MCI that persist during AD. Our goal is to uncover transcriptomic changes that could support earlier diagnosis and the development of more effective therapeutic strategies, starting from the early stages of the disease, to slow down or mitigate its progression. Our work provides a consistent picture of the transcriptomic unbalance of many genes strongly involved in ribosomal formation and biogenesis and splicing processes both in patients with MCI and with AD. Full article

In this study, the combined influence of vibration direction, feature selection strategy, and the support vector machine (SVM) kernel on the classification accuracy of unbalance faults was investigated. Experiments were carried out on a Jeffcott rotor test rig at a constant speed and under three operating conditions. The overlapping sliding window method was used for raw sample expansion. Features extracted from time domain signals and from the order and power spectra obtained in the frequency domain were ranked using the Kruskal–Wallis algorithm. Based on the feature-ranking results, the three most discriminative features for each domain–axis combination, as well as all nine most discriminative features for each axis in a hybrid manner, were fed into SVM classifiers with different kernels, and their performance was evaluated using ten-fold cross-validation. Classification using vibration signals in the vertical direction had higher accuracy rates than those using signals in the horizontal direction for the feature sets obtained in the same domains. According to the statistical results, feature set selection had a much greater impact on classification accuracy than SVM kernel choice. Power spectrum-based features allowed higher classification accuracies in all SVM algorithms compared to both the time domain features and the order spectrum-based features for detecting unbalance faults. Increasing the number of features or employing hybrid feature selection did not result in a consistent or significant enhancement in overall classification performance. Selecting the right SVM kernel shapes both the model’s flexibility and its fit to the chosen feature space; when this fit is inadequate, classification accuracy may decrease. Consequently, by selecting the appropriate vibration direction, feature set, and SVM kernel, an improvement of up to 67% in unbalance fault classification accuracy was achieved. Full article

With the increasing penetration of distributed generation and the diversification of electrical equipment, distribution networks face issues like three-phase unbalance and harmonic currents, while the voltage stability and inertia of the grid-connected system also decrease. A certain amount of energy storage is needed in a Distribution Static Synchronous Compensator (DSTATCOM) to manage power quality and actively support voltage and inertia in the network. This paper first addresses the limitations of traditional $dq0$ compensation algorithms in effectively filtering out negative-sequence twice-frequency components. An improved $dq0$ compensation algorithm is proposed to reduce errors in detecting positive-sequence fundamental current under unbalanced three-phase conditions. Second, considering the impedance ratio characteristics of the distribution network, while reactive power voltage regulation is common, active power regulation is more effective in high-resistance distribution networks. A grid-forming model-based active and reactive power coordinated voltage regulation method is proposed. This method uses synchronous control to establish a virtual three-phase voltage internal electromotive force, forming a comprehensive compensation strategy that combines power quality improvement and active voltage support, exploring the potential of energy storage DSTATCOM applications in distribution networks. Finally, simulation and experimental results demonstrate the effectiveness of the proposed control method. Full article

This paper focuses on the influence of torsional loading on the vibration-based unbalance fault diagnosis technology under variable-speed conditions. The coupled flexural–torsional nonstationary governing equations of motion are obtained and solved numerically. Taking the short-time chirp Fourier transform from the acceleration signal, which is determined from the numerical solutions, the influence of variable loading on the magnitude of the fundamental rotational harmonic—a diagnostic feature for conventional unbalance diagnosis technology—as well as its speed-invariant version for novel unbalance diagnosis technology is assessed. Numerical assessment shows that despite the stationary conditions, where the first rotational harmonic magnitude is independent from the torsional load, the conventional unbalance technology depends on the variable torsional load. However, the novel speed-invariant diagnostic technology is independent of the variable torsional load. The dependency of the conventional unbalance fault diagnosis technology on the variable torsional load and the independency of the novel speed-invariant unbalance diagnostic technology on the variable loading are justified by performing thorough experimental investigations on a variable-speed wind turbine with a permissible level of unbalance. Full article

Background. The venom of Loxoscelesrufescens (L.r.), also known as the violin and/or brown spider, contains a wide variety of proteins and can induce a complex, intense, and uncontrolled inflammatory response, hemolysis, thrombocytopenia, dermo-necrosis, and renal failure. Studies have postulated the efficacy of hyperbaric oxygen therapy (HBOT) for Loxosceles bites. However, data describing the use and beneficial effects of HBO are, to date, relatively scarce. Only a few cases of Loxosceles bites in Northern Italy have been documented, and there is no laboratory test available for the diagnosis. Objectives. We present seven cases (aged 54.5 ± 4.2 years) of patients who presented to the emergency room (E.R.) of Niguarda Hospital in Milan from March to October 2022. Methods. Blood and urine samples were collected and biomarkers of oxidative stress (OxS) (reactive oxygen species (ROS), total antioxidant capacity (TAC), lipid peroxidation (8iso-PFG2α), DNA damage (8-OH-dG)), inflammation (IL-6, IL-1β, TNF-α, sICAM1), and renal function (creatinine, neopterin, uric acid) before (T0), during (T1, T2), and after (1–2 wk T3–T4; 1 month T5) the HBOT treatment (US Navy Treatment Table 15 protocol) were studied. Results. At T0, patients showed a significant unbalance of OxS; high levels of ROS, 8-isoPGF2α, and inflammatory status (IL-6, TNF-α; sICAM); and a low level of antioxidant capacity. At the end of HBOT (T2), a significant reduction in Oxy-inflammation levels over time—8-iso −26%, 8-OH-dG −9%, IL-6 −71%, IL-1bβ −12%, TNF-α −13%, and sICAM1 −17%—associated with clinical improvement was shown. Conclusions. These reductions, along with those in renal function markers, mirrored the observed improvement in the evolution of the skin lesion and the patients’ self-reported general wellness and pain. In conclusion, HBOT should be considered a valuable therapeutic tool after L.r. bites. Full article

For a new distribution network with energy storage and a flexible multi-state switch (FMSS), several problems of multi-mode operation and switching, such as the unbalance of feeder loads and feeder faults, among others, should be considered. This paper forwards a coordination control strategy to address the above challenges faced by the FMSS under grid-connected operations. To tackle the multi-mode operation problem, the system’s operational state is divided into multiple working modes according to the operation states of the system, the positions and number of fault feeders, the working states of the transformers, and the battery’s state of charge. To boost the system’s operational reliability and load balance and extend the power supply time for the fault load, the appropriate control objectives in the coordination control layer and control strategies in the equipment layer for different working modes are established for realizing the above multi-directional control objectives. To resolve the phase asynchrony issue among the fault load and other normal working loads caused by the feeder fault, the off-grid phase-locked control based on the V/f control strategy is applied. To mitigate the bus voltage fluctuation caused by the feeder fault switching, the switching control sequence for the planned off-grid is designed, and the power feed-forward control strategy of the battery is proposed for the unplanned off-grid. The simulation results show that the proposed control strategy can ensure the system’s power balance and yield a high-quality flexible power supply during the grid-connected operational state. Full article

This article presents the findings of an experimental study conducted on a vertical axis wind turbine (VAWT) tower instrumented with cascaded fiber Bragg grating (FBG) sensors to detect bending deformations. Structural health monitoring (SHM) is an essential need in the industry to reduce costs and maintenance time, and to prevent machine failures. First, FBG strain sensors were glued vertically along the tower to investigate the sensors behavior as a function of their height. The maximum signal-to-noise ratio is obtained when FBGs are placed at the tower base. Then, four packages were installed inside the tower, at the base, according to four cardinal directions. Each package contains an FBG strain sensor, and an extra temperature FBG for discrimination. The use of easy-to-deploy packages is a must for industrial installations. Afterwards, by using power spectral density (PSD) on the strain signals, three sources of tower oscillations are discovered: wind force, structure unbalance, and 1st tower mode resonance, each with its intrinsic frequency. Wind force and structure unbalance cause mechanical stresses at a frequency proportional to the wind turbine rotational speed, while the 1st tower mode frequency depends only on the machine geometry, regardless of the rotational speed. This study also analyzes the deformation amplitude for different rotational rates within the VAWT operational range (10–35 rpm). The resonance amplitude depends on the proximity of the rotational rate to the resonant frequency (22 rpm) and the duration at that rate. For structure unbalance, the oscillation amplitude increases with the rotational rate, due to the centrifugal effect. It is supposed that wind force deformation amplitude naturally depends on wind speed, which is unpredictable at a given precise time. The results of our experimental observations are very valuable for both the wind turbine manufacturer and owner. Full article

The integration of Artificial Intelligence into industrial maintenance remains challenging due to the scarcity of high-quality data representing faulty conditions. Machine Learning models trained on laboratory testbed data often fail to generalize effectively in real workshop environments. This study evaluated the effectiveness of Transfer Learning models in handling this domain shift challenge compared with Machine Learning models. Their potential to address the generalization gap was assessed by analyzing the model adaptability from lab-recorded data to data from emulated workshop conditions, where real-world variability was replicated by embedding synthetic noise into the lab-recorded data. The case study focuses on detecting rotor unbalance through bearing vibration signals at varying speeds. A Support Vector Classifier was trained on the transformed features for both models for binary classification. Model performance was assessed under varying data availability and noise conditions to evaluate the impact of these factors on classification accuracy, sensitivity, and specificity. The results show that Transfer Learning outperforms Machine Learning, achieving up to 30% higher accuracy under high-noise conditions. Although the Machine Learning model exhibits greater sensitivity, it misclassifies balanced cases and reduces specificity. In contrast, the Transfer Learning model maintains high specificity but has difficulty detecting mild unbalance levels, particularly when data availability is limited. Full article

To ensure the vibration safety of rotor support systems in modern aeroengines, this study develops a dynamic model of the aeroengine gas generator rotor system and analyzes its complex unbalance response characteristics. Subsequently, it investigates vibration reduction strategies based on these response patterns. This study begins by developing individual dynamic models for the disk–blade system, the circular arc end-teeth connection structure and the squeeze film damper (SFD) support system. These models are then integrated using the differential quadrature finite element method (DQFEM) to create a comprehensive dynamic model of the gas generator rotor system. The unbalance response characteristics of the rotor system are calculated and analyzed, revealing the impact of the unbalance mass distribution and the combined support system characteristics on the unbalance response of the rotor system. Drawing on the obtained unbalance response patterns, the vibration reduction procedures for the rotor support system are explored and experimentally verified. The results demonstrate that the vibration response of the modern aeroengine rotor support system can be reduced by adjusting the unbalance mass distribution, decreasing the bearing stiffness and increasing the bearing damping, thereby enhancing the vibration safety of the rotor system. This study introduces a novel integration of DQFEM with detailed component-level modeling of circular arc end-teeth connections, disk–blade interactions and SFD dynamics. This approach uniquely captures the coupled effects of unbalance distribution and support system characteristics, offering a robust framework for enhancing vibration safety in aeroengine rotor systems. The methodology provides both theoretical insights and practical guidelines for optimizing rotor dynamic performance under unbalance-induced excitations. Full article

With the increasing integration of Inverter-Based Resources (IBRs) into power grids, accurate estimation of line impedance between the Point of Connection (POC) and the Point of Measurement (POM) has become critical to ensure stable and efficient reactive power control. However, conventional impedance estimation methods often face challenges such as power quality degradation and sensitivity to voltage unbalance. This paper presents a method to improve the reactive power control performance of Inverter-Based Resources (IBRs) by estimating the line impedance between the Point of Connection (POC) and the Point of Measurement (POM) and utilize the estimated impedance into control. The impact of voltage drop caused by line impedance on reactive power delivery is analyzed, and a compensation method is designed to mitigate the resulting control errors. The line impedance is estimated through a negative-sequence current injection technique, under the condition that the voltage phases at the two measurement points are synchronized. To address potential voltage unbalance issues that may arise during the injection process, a dedicated compensation algorithm is also proposed. The proposed algorithm is validated through both simulations and lab-scale experiments, demonstrating that the line impedance can be estimated with an error of less than 2%, while effectively compensating for reactive power distortion at the POM. Full article

This study introduces a flywheel rotor support structure for an active magnetic suspension flywheel energy storage system. In this structure, there is an axial offset between the axial-bearing position and the mass-center of the flywheel rotor, which affects the tilting rotation of the flywheel rotor and which causes the coupling between its tilting rotation and radial motion. Therefore, the influence of the bearing position on the vibration characteristics of the flywheel rotor is explored in this paper. The tilting rotation constraint of the flywheel rotor by axial active magnetic bearing (AAMB) is analyzed, and the radial active magnetic bearing (RAMB) is equivalently treated with dynamic stiffness and dynamic damping. Based on this, a dynamic model of the active magnetic suspension rigid flywheel rotor, considering the position parameter of the axial bearing, is established. To quantify the axial offset between the position of the AAMB and the mass-center of the flywheel rotor, the axial-bearing position offset ratio γ is defined. The variation trend of the vibration characteristics of flywheel rotor with γ is discussed, and its correctness is validated through experiments. It is indicated that, with the increase of γ, the second-order positive precession frequency of the flywheel rotor decreases obviously, and the influence of the gyroscope torque gradually weakens. Meanwhile, its second-order critical speed ω_2c decreases significantly (when γ is 0.5, ω_2c decreases by about 62%); ω_2c corresponds to the inclined mode, revealing that the offset ratio γ has a prominent influence on the critical speed under this mode. In addition, as γ increases, the mass unbalance response amplitude of the flywheel rotor under the speed of ω_2c decreases significantly. The reasonable design of the axial-bearing position parameter can effectively improve the operational stability of the active magnetic suspension flywheel energy storage system. Full article