Search Results (4,560)

This study delves into the response characteristics of core grounding current signals in power transformers across different operating conditions, aiming to enhance the accuracy of transformer condition assessment. Existing detection technologies often rely on single-parameter methods, which fall short in providing a comprehensive evaluation of transformer conditions. To address this limitation, this research develops a wideband circuit model based on multi-conductor transmission line theory and backed by experimental validation. The model systematically investigates the response mechanisms of core grounding current to various electrical stresses, including impulse voltages, power-frequency harmonics, and partial discharges. The findings reveal distinct response characteristics of core grounding current under different stresses. Under impulse voltage excitation, the core current exhibits high-frequency oscillatory decay with characteristics linked to voltage waveform parameters. In harmonic conditions, the current spectrum shows linear correspondence with excitation voltages, with no resonance below 1 kHz. Partial discharges induce high-frequency oscillations in the grounding current due to multi-resonant networks formed by distributed winding-core parameters. This study establishes a new theoretical framework for transformer condition assessment based on core grounding current analysis, offering critical insights for optimizing detection technologies and overcoming the limitations of traditional methods. Full article

Adaptive Linear Neuron (ADALINE) is a well-known neural network method that has been utilized for generating a reference current intended to regulate the operation of shunt-typed active harmonic filters (SAHFs). These filters are essential for improving power quality by mitigating harmonic disturbances and restoring current waveform symmetry in power systems. While the latest variant, Simplified ADALINE, offers notable advantages over its predecessors, such as a reduced complexity and faster learning speed, its performance has primarily been evaluated under stable grid conditions, leaving its performance under distorted environments largely unexplored. To address this gap, this work introduces two key modifications to the Simplified ADALINE framework: (1) the integration of a new phase-tracking algorithm based on the concept of orthogonality and selective filtering, and (2) transitioning from the direct current control (DCC) to an indirect current control (ICC) mechanism. Test environments featuring distorted grids and nonlinear rectifier loads are simulated in MATLAB/Simulink software to evaluate the performance of the proposed method against the existing Simplified ADALINE method. The key findings demonstrate that the proposed method effectively handled harmonic distortion and noise disturbance. As a result, the associated SAHF achieved an additional reduction in %THD (by 10.77–13.78%), a decrease in reactive power (by 58.3 VAR–67 VAR), and improved grid synchronization with a smaller phase shift (by 0.9–1.2°), while also maintaining proper waveform symmetry even in challenging grid conditions. Full article

Monitoring critical transmission sections is essential for ensuring the operational security of power grids. This paper proposes a systematic method to identify critical transmission sections using the maximum flow–minimum cut theorem. The approach begins by representing the power grid as an undirected graph and identifying its hanging nodes. The network is then partitioned into several undirected subgraphs based on identified cut points. Each subgraph is transformed into a flow network according to actual power flow data. An efficient minimum cut set search algorithm is developed to locate potential transmission sections. To assess the risk under extreme conditions, a mixed-integer optimization model is formulated to select sections that are vulnerable to overload-induced tripping during N-K line outages caused by natural disasters. Simulation results on the IEEE RTS 24-bus and IEEE 39-bus systems validate the effectiveness and applicability of the proposed method. Full article

The paradigm of reinforcement learning (RL) refers to agents that learn iteratively through continuous interactions with their environment. However, when the value function is unknown, a neural network is used, which is typically encoded into an unknown temporal difference equation. When RL is implemented in physical systems, explicit convergence and stability analyses are required to guarantee the worst-case operations for any trial, even when the initial conditions are set to zero. In this paper, physical RL (p-RL) refers to the application of RL in dynamical systems that interact with their environments, such as robot manipulators in contact tasks and humanoid robots in cooperation or interaction tasks. Unfortunately, most p-RL schemes lack stability properties, which can even be dangerous for specific robot applications, such as those involving contact (constrained) tasks or interaction tasks. Considering an unknown and disturbed DAE2 robot, in this paper a p-RL approach is developed to guaranteeing robust stability throughout a continuous-time-adaptive actor–critic, with local exponential convergence of force–position tracking error. The novel adaptive mechanisms lead to robustness, while an integral sliding mode enforces tracking. Simulations are presented and discussed to show our proposal’s effectiveness, and some final remarks are addressed concerning the structural aspects. Full article

Transformers are critical equipment in power transmission and distribution systems, and the condition of their windings significantly impacts their reliable operation. Therefore, the fault diagnosis of transformer windings is of great importance. Addressing the challenge of limited fault samples in traditional diagnostic methods, this study proposes a small-sample fault diagnosis method for transformer windings. This method combines data augmentation using the Gramian angular field (GAF) and generative adversarial networks (GAN) with a deep residual network (ResNet). First, by establishing a transformer winding fault simulation experiment platform, frequency response curves for three types of faults—axial displacement, bulging and warping, and cake-to-cake short circuits—and different fault regions were obtained using the frequency response analysis method (FRA). Second, a frequency response curve image conversion technique based on the Gramian angular field was proposed, converting the frequency response curves into Gramian angular summation field (GASF) and Gramian angular difference field (GADF) images using the Gramian angular field. Next, we introduce several improved GANs to augment the frequency response data and evaluate the quality of the generated samples. We compared and analysed the diagnostic accuracy of ResNet34 networks trained using different GAF–GAN combination datasets for winding fault types, and we proposed a transformer winding small-sample fault diagnosis method based on GAF-GAN-ResNet34, which can achieve a fault identification accuracy rate of 96.88% even when using only 28 real samples. Finally, we applied the proposed fault diagnosis method to on-site transformers to verify its classification performance under small-sample conditions. The results show that, even with insufficient fault samples, the proposed method can achieve high diagnostic accuracy. Full article

Coal mining disturbance induces progressive damage and fracturing in overlying rock (OLR), forming a complex fracture network. This process triggers groundwater depletion, ecological degradation, and severely compromises mine safety. Based on field drilling sampling and mechanical experiments, this paper reveals the occurrence properties and characteristics of weakly cemented overlying rock (WCOLR). At the same time, similar simulation experiments, DIC speckle analysis system, and fractal theory are used to explain the development and evolution mechanism of mining-induced fractures under this special geological condition. The OLR fracture is determined based on the grid fractal dimension (D) distribution. A stress arch-bed separation (BS) co-evolution model is established based on dynamic cyclic BS development and stress arch characteristics, enabling identification of BS horizons. The results show that the overlying weak and extremely weak rock accounts for more than 90%. During the process of longwall face (LF) advancing, the D undergoes oscillatory evolution through five distinct stages: rapid initial growth, constrained slow growth under thick, soft strata (TSS), dimension reduction induced by fracturing and compaction of TSS, secondary growth from newly generated fractures, and stabilization upon reaching full extraction. Grid-based D analysis further categorizes fracture zones, indicating a water conducting fracture zone (WCFZ) height of 160~180 m. Mining-induced fractures predominantly concentrate at dip angles of 0–10°, 40–50°, and 170–180°. Horizontally BS fractures account for 70.2% of the total fracture population, vertically penetrating fractures constitute 13.1% and transitional fractures make up the remaining 16.7%. The stress arch height is 314.4 m, and the stable BS horizon is 260 m away from the coal seam. Finally, an elastic foundation theory-based model was used to predict BS development under top-coal caving operations. This research provides scientific foundations for damage-reduced mining in ecologically vulnerable Western China coalfields. Full article

The rapid expansion of device connectivity and the increasing demand for data traffic have become pivotal aspects of our daily lives, especially within the Internet of Things (IoT) ecosystem. Consequently, operators are striving to identify the most innovative and robust solutions capable of accommodating these escalating requirements. The emergence of the sliced fifth-generation mobile network (sliced 5G) offers a promising architecture that leverages a novel Radio Access Technology known as New Radio (NR), promising significantly enhanced data rate experiences. By integrating the network slicing (NS) architecture, greater flexibility and isolation are introduced into the preexisting infrastructure. The isolation effect of NS is particularly advantageous in mitigating interference between slices, as it empowers each slice to function independently. This paper addresses the user association challenge within a sliced 5G (NR)-IoT network. To this end, we present an Unconstrained-Markov Decision Process (U-MDP) model formulation of the problem. Subsequently, we propose the U-MDP association algorithm, which aims to determine the optimal user-to-slice associations. Unlike existing approaches that typically rely on static user association or separate optimization strategies, our U-MDP algorithm dynamically optimizes user-to-slice associations within a sliced 5G-IoT architecture, thereby enhancing adaptability to varying network conditions and improving overall system performance. Our numerical simulations validate the theoretical model and demonstrate the effectiveness of our proposed solution in enhancing overall system performance, all while upholding the quality of service requirements for all devices. Full article

In complex traffic environments, image degradation due to adverse factors such as haze, low illumination, and occlusion significantly compromises the performance of object detection systems in recognizing vehicles and pedestrians. To address these challenges, this paper proposes a robust visual detection framework that integrates multi-stage image enhancement with a lightweight detection architecture. Specifically, an image preprocessing module incorporating ConvIR and CIDNet is designed to perform defogging and illumination enhancement, thereby substantially improving the perceptual quality of degraded inputs. Furthermore, a novel enhancement strategy based on the Horizontal/Vertical-Intensity color space is introduced to decouple brightness and chromaticity modeling, effectively enhancing structural details and visual consistency in low-light regions. In the detection phase, a lightweight state-space modeling network, Mamba-Driven Lightweight Detection Network with RT-DETR Decoding, is proposed for object detection in complex traffic scenes. This architecture integrates VSSBlock and XSSBlock modules to enhance detection performance, particularly for multi-scale and occluded targets. Additionally, a VisionClueMerge module is incorporated to strengthen the perception of edge structures by effectively fusing multi-scale spatial features. Experimental evaluations on traffic surveillance datasets demonstrate that the proposed method surpasses the mainstream YOLOv12s model in terms of mAP@50–90, achieving a performance gain of approximately 1.0 percentage point (from 0.759 to 0.769). While ensuring competitive detection accuracy, the model exhibits reduced parameter complexity and computational overhead, thereby demonstrating superior deployment adaptability and robustness. This framework offers a practical and effective solution for object detection in intelligent transportation systems operating under visually challenging conditions. Full article

For aircraft departure, which is a process of multi-resource coordination, strict time limitations, and complex condition constraints, the optimization of taxi-out time prediction is critical for enhancing airport surface operational efficiency, optimizing runway slot utilization, and reducing aircraft ground delay and fuel consumption. By combining aircraft taxi path and network traffic flow features, a refined airport road-level transport network model is constructed to accurately characterize the taxi path topology and node-edge attributes. On this basis, two new micro-features are introduced: estimated taxi time and the number of handovers. Experimental results show that after the introduction of the micro-features, the prediction accuracy of the taxi-out time prediction model within the error of 1 min increases from 49.29% to 54.41%, and the prediction accuracy within the error of 5 min reaches 99.42%. This method effectively addresses the limitations of traditional models that focus solely on the overall taxiing process while neglecting microscopic airfield network dynamics and time consumption during control handover procedures. The method can be integrated into the Airport Collaborative Decision Making (A-CDM) system to provide minute-level support for departure taxi-out time prediction, thereby providing a more precise and operationally aligned temporal benchmark for intelligent apron operations scheduling, aircraft sequencing optimization, and other collaborative decision making processes. Full article

Rapid pressure fluctuations—known as hydraulic transients—occur during valve operations or load changes in turbines and pumps. The presence of biofouling, particularly caused by the golden mussel (Limnoperna fortunei), can intensify these effects and compromise the structural integrity of pressurized systems. This study experimentally evaluated the influence of such biofouling on pressure peaks during transient events in forced conduits. A hydraulic test rig was developed using PVC pipes with nominal diameters of 2½”, 3”, and 4”, tested under both clean conditions and with simulated biofouling printed in 3D, replicating mussel morphology. Results showed that, under the same initial flow rates, pressure peaks in biofouled pipes were significantly higher than in clean ones, especially in smaller diameters. To mitigate structural risks, the downstream shut-off valve closure time was modulated using a needle valve, effectively reducing peak pressures to levels closer to design limits. It is concluded that L. fortunei colonization alters transient hydraulic behavior and should be considered in the design and operation of systems vulnerable to biofouling, particularly in critical infrastructure such as water supply networks and hydroelectric power plants. Full article

This paper introduces a speed-sensorless dual-field-oriented control (DFOC) strategy for induction motors (IMs). DFOC combines the advantages or rotor- and stator-field orientation to significantly reduce the parameter sensitivity of the control regarding the generation of the converter control variable. A simplified structure is also proposed, using only two regulators for the flux and speed control, eliminating the two current regulators. Related to sensorless control, the classical adaptation mechanism within an MRAS (model reference adaptive system) observer is replaced with artificial intelligence (AI)-based approaches. Specifically, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are employed for rotor speed estimation. They offer significant advantages in managing complex and nonlinear systems, providing enhanced flexibility and adaptability compared to traditional MRAS methods. The effectiveness of the proposed sensorless control scheme is validated through both simulation and real-time implementation. The paper focuses on the ANN and RNN architectures, as deep learning models, in terms of the reliability and accuracy of rotor speed estimation under various operating conditions. Full article

Under cool and moist climates, timely implementation of ditch network maintenance (DNM) is crucial for sustaining productivity of drained forests, thus reducing operational costs, while mitigating environmental risks. This underscores the need to understand tree growth responses to DNM. This study evaluated the effects of DNM on tree radial increment in sites with both organic and mineral drained soils, focusing on regionally commercially important species: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and silver birch (Betula pendula). Responses of relative growth changes over eight years post-DNM to site and tree characteristics were assessed using a linear mixed-effects model. Species- and site-specific growth responses to DNM were indicated by significant interactions between tree species, site type, and distance from the drainage ditch. While growth responses were generally neutral (non-significant), variability among sites and species suggests that both organic and mineral soils might be prone to site-level moisture depletion near drainage infrastructure in the post-DNM period. The effect of stand age and density suggested higher responsiveness of older and less dense stands, hence positive effects of thinning to resilience of stands to DNM. These findings highlight the importance of adapting DNM strategies to local site conditions and stand characteristics to minimize drought-related growth limitations. Full article

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article

This field study describes the successful implementation and evaluation of a Polymer-free Delayed Acid System, a next-generation acid retarder system that is chemically superior to traditional emulsified acid systems with an amphoteric-based surfactant. It is a polymer-free system that stimulates ultra-tight carbonate reservoirs in extreme sour and high-temperature conditions. The candidate well, located in an onshore gulf region field, for a major oil and gas company demonstrated chronically unstable production behavior for over two years, with test volumes fluctuating unpredictably between 200 and 400 barrels of oil per day. This indicated severe near-wellbore damage, high skin, and limited matrix permeability (<0.3 mD). The well was chosen for a pilot trial of the Polymer-free Delayed Acid System technology after a thorough formation study, which included mineralogical characterization and capillary diagnostics. The innovative acid retarder formulation, designed for deep matrix penetration and controlled acid–rock reaction, uses intrinsic encapsulation kinetics to significantly increase the acid’s reactivity, allowing it to bypass damaged zones, minimize acid leak-off, and initiate dominant wormhole propagation into the tight formation. The stimulation procedure began with a custom pre-flush designed to change nanoscale wettability and interfacial tension, so increasing acid displacement and assuring effective contact with the formation rock. Real-time injectivity testing and operational data collecting were performed prior to, during, and following the acid job, with pre-stimulation injectivity peaking at 1.2 bpm, indicating poor formation conductivity. Treatment with the Polymer-free Delayed Acid System resulted in a 592% increase in post-stimulation injectivity, indicating significant increases in near-wellbore permeability and successful propagation. However, a substantial operational difficulty arose: the well remained shut down for more than two months following the acid stimulation work due to surface infrastructure delays, notably the scheduling and execution of a flowline cleanup campaign. This lengthy closure slowed immediate flowback analysis and impeded direct assessment of treatment performance because production could not be tracked in real time. Despite this, once the surface system was operational and the well was open to flow, a structured production testing program was carried out over four quarterly intervals. The well regularly produced at an average stable rate of 500 bbl/day, more than doubling pre-treatment performance and demonstrating the long-term effectiveness and mechanical durability of the acid-induced wormhole network. Despite the post-job shut-in, the Polymer-free Delayed Acid System maintained the stimulating impact even under non-ideal settings, demonstrating its robustness. The Polymer-free Delayed Acid System outperforms conventional emulsified acid systems, giving better control over acid placement and reactivity, especially under severe reservoir conditions with bottomhole temperatures reaching 200 °F. This project offers a field-proven methodology that combines advanced chemical engineering, formation-specific design, and live diagnostics, as well as a scalable blueprint for unlocking hydrocarbon potential in similarly complicated, low-permeability reservoirs. Full article

Supercritical carbon dioxide (Sc-CO₂) thermal shock fracturing emerges as an innovative rock fragmentation technology combining environmental sustainability with operational efficiency. This study establishes a thermo-hydro-mechanical coupled model to elucidate how in situ stress magnitude and anisotropy critically govern damage progression and fluid dynamics during Sc-CO₂ thermal shock fracturing. Key novel findings reveal the following: (1) The fracturing mechanism integrates transient hydrodynamic shock with quasi-static pressure loading, generating characteristic bimodal pressure curves where secondary peak amplification specifically indicates inhibited interwell fracture coalescence under anisotropic stress configurations. (2) Fracture paths undergo spatiotemporal reorientation—initial propagation aligns with in situ stress orientation, while subsequent growth follows thermal shock-induced principal stress trajectories. (3) Stress heterogeneity modulates fracture network complexity through confinement effects: elevated normal stresses perpendicular to fracture planes reduce pressure gradients (compared to isotropic conditions) and delay crack initiation, yet sustain higher pressure plateaus by constraining fracture connectivity despite fluid leakage. Numerical simulations systematically demonstrate that stress anisotropy plays a dual role—enhancing peak pressures while limiting fracture network development. This demonstrates the dual roles of the technology in enhancing environmental sustainability through waterless operations and reducing carbon footprint. Full article