Search Results (517)

The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation. Full article

Among various failure causes, lightning overvoltage represents the most significant threat to overhead distribution lines, which serve as critical components in power systems. This study uses the hybrid partial element equivalent circuit (PEEC) multi-conductor transmission line (MTL) method to perform overvoltage simulations and investigate lightning risk distribution along distribution lines developed from a real 10 kV distribution networks in Guizhou, China. The results of the rocket-triggered lightning observation verify the accuracy of the hybrid method for direct lightning simulation. Combining the Monte Carlo method with the electro-geometric model (EGM), the impact of differential protection configurations on annual lightning flashover rates is analyzed. The results demonstrate that lightning strikes on phase wires generate high-magnitude overvoltages but with limited spatial influence, resulting in fewer pole flashovers. Conversely, strikes on poles produce lower overvoltage peaks but affect wider areas, leading to significantly more flashovers. Using annual flashover rates as the risk evaluation metric, the line topologies into high-risk, medium-risk, and other low-risk areas are classified. Targeting an annual flashover rate below 0.4 as the design objective, the configuration schemes of the arresters are progressively optimized. This risk-based approach provides an effective reference framework for differential protection design of distribution line safeguards. Full article

Line feature matching is a fundamental and extensively studied subject in the fields of photogrammetry and computer vision. Traditional methods, which rely on handcrafted descriptors and distance-based filtering outliers, frequently encounter challenges related to robustness and a high incidence of outliers. While some approaches leverage point features to assist line feature matching by establishing the invariant geometric constraints between points and lines, this typically results in a considerable computational load. In order to overcome these limitations, we introduce a novel Bayesian posterior probability framework for line matching that incorporates three geometric constraints: the distance between line feature endpoints, midpoint distance, and angular consistency. Our approach initially characterizes inter-image geometric relationships using Fourier representation. Subsequently, we formulate the posterior probability distributions for the distance constraint and the uniform distribution based on the constraint of angular consistency. By calculating the joint probability distribution under three geometric constraints, robust line feature matches are iteratively optimized through the Expectation–Maximization (EM) algorithm. Comprehensive experiments confirm the effectiveness of our approach: (i) it outperforms state-of-the-art (including deep learning-based) algorithms in match count and accuracy across common scenarios; (ii) it exhibits superior robustness to rotation, illumination variation, and motion blur compared to descriptor-based methods; and (iii) it notably reduces computational overhead in comparison to algorithms that involve point-assisted line matching. Full article

In this study, data on the characteristics of overhead power lines of high voltage was used in a classification task to predict power supply outages by means of a supervised machine learning technique. In order to choose the most optimal features for power outage prediction, an Exploratory Data Analysis on power line parameters was carried out, including statistical and correlational methods. For the given task, five classifiers were considered as machine learning algorithms: Support Vector Machine, Logistic Regression, Random Forest, and two gradient-boosting algorithms over decisive trees LightGBM Classifier and CatBoost Classifier. To automate the process of data conversion and eliminate the possibility of data leakage, Pipeline and Column Transformers (builder of heterogeneous features) were applied; data for the models was prepared using One-Hot Encoding and standardization techniques. The data were divided into training and validation samples through cross-validation with stratified separation. The hyperparameters of the classifiers were adjusted using optimization methods: randomized and exhaustive search over specified parameter values. The results of the study demonstrated the potential for predicting power failures on 110 kV overhead power lines based on data on their parameters, as can be seen from the derived quality metrics of tuned classifiers. The best quality of outage prediction was achieved by the Logistic Regression model with quality metrics ROC AUC equal to 0.78 and AUC-PR equal to 0.68. In the final phase of the research, an analysis of the influence of power line parameters on the failure probability was made using the embedded method for determining the feature importance of various models, including estimating the vector of regression coefficients. It allowed for the evaluation of the numerical impact of power line parameters on power supply outages. Full article

High-Voltage Direct Current (HVDC) systems are transforming the global energy landscape, distinguished by their efficiency, stability, and low impact on the electrical grid. One of the challenges of HVDC transmission line design is assessing the generated stray magnetic field, as it can have negative effects on human health and the environment. This study presents an analytical methodology for calculating the magnetic field density at any point along an HVDC line corridor. It considers the spatial configuration, the current per pole, and the location of the conductors. The equations allow for the calculation of the horizontal and vertical components of the field, as well as its total magnitude. A practical case study of a ±500 and ±600 kV HVDC two-pole transmission line is presented. The methodology was programmed in MATLAB^® version R2024a to calculate the magnetic field density, and the results are consistent with those obtained with the established methodology. The presented methodology can be applied to monopolar and two-pole HVDC overhead transmission lines, offering speed and accuracy. Full article

Electrified roads represent an emerging transportation solution in the context of global energy transition. These systems enable vehicles equipped with roof-mounted pantographs to draw power from overhead contact lines while in motion, allowing continuous energy replenishment. The effectiveness of this energy transfer—namely, the quality of pantograph–catenary interaction—is significantly influenced by the pantograph’s equivalent mechanical parameters. This study develops a three-dimensional overhead catenary model and a five-mass pantograph model tailored to electrified roads. Under conditions of road surface irregularities, it investigates how variations in equivalent pantograph parameters affect key contact performance indicators. Simulation results are used to identify a new set of equivalent pantograph parameters that significantly improve the overall quality of pantograph–catenary interaction compared to the baseline configuration. Sensitivity analysis further reveals that, under road-induced excitation, pan-head stiffness is the most critical factor affecting contact performance, while pan-head damping, upper frame stiffness, and upper frame damping show minimal influence. By constructing a coupled dynamic model and conducting parameter optimization, this study elucidates the role of key pantograph parameters for electrified roads in determining contact performance. The findings provide a theoretical foundation for future equipment development and technological advancement. Full article

Dynamic Line Rating (DLR) is increasingly important for maximizing capacity of existing overhead transmission lines. Conventional thermal rating methods, such as IEEE 738 and model conductors as single, isothermal cylinders and offer limited guidance for multi-conductor bundles, not fully capturing the complex aerodynamic and thermal interactions present in high-voltage networks. This study addresses these limitations by presenting a high-fidelity, two-dimensional coupled thermal-fluid model developed in COMSOL Multiphysics 4.3b. Single and bundled configurations (two-conductor, three-conductor and four-conductor) are analyzed under steady-state conditions using the Shear Stress Transport (SST) turbulence model, accounting for sub-conductor spacing, wind speed, and interactions between temperature distribution and airflow. Simulation results are compared with ampacity calculations from relevant standards to evaluate limitations of simplified models. Results show that leeward conductors reach temperatures up to ~4 °C higher than windward conductors, forming the thermal bottleneck, with peak temperatures of ~103.3 °C versus ~99 °C for single conductors. For bundled conductors, the current required to keep the maximum temperature at 100 °C was calculated, and this value was found to be approximately 3% lower than the current predicted by IEEE 738. The study emphasizes the importance of multiphysics, position-aware simulations to prevent overloading and optimize transmission line utilization. Full article

Network monitoring is becoming increasingly challenging as networks grow in scale, speed, and complexity. The evolution of monitoring approaches reflects a shift from device-centric, localized techniques toward network-wide observability enabled by modern networking paradigms. Early methods like SNMP polling and NetFlow provided basic insights but struggled with real-time visibility in large, dynamic environments. The emergence of Software-Defined Networking (SDN) introduced centralized control and a global view of network state, opening the door to more coordinated and programmable measurement strategies. More recently, programmable data planes (e.g., P4-based switches) and in-band telemetry frameworks have allowed fine grained, line rate data collection directly from traffic, reducing overhead and latency compared to traditional polling. These developments mark a move away from single point or per flow analysis toward holistic monitoring woven throughout the network fabric. In this survey, we systematically review the state of the art in network-wide monitoring. We define key concepts (topologies, flows, telemetry, observability) and trace the progression of monitoring architectures from traditional networks to SDN to fully programmable networks. We introduce a taxonomy spanning local device measures, path level techniques, global network-wide methods, and hybrid approaches. Finally, we summarize open research challenges and future directions, highlighting that modern networks demand monitoring frameworks that are not only scalable and real-time but also tightly integrated with network control and automation. Full article

Railway service interruptions and electrical hazards often arise due to bird nests concealed within the intricate, highly symmetric overhead catenary networks of high-speed lines. These nests are difficult to pinpoint automatically, not only because they are diminutive and often merge visually with the surroundings but also due to occlusions and the persistent lack of substantial labeled datasets. To address this bottleneck, this work presents the High-Speed Railway Catenary Nest Dataset (HRC-Nest), merging 800 authentic images and 1000 synthetic samples to capture a spectrum of scenarios. Building on the symmetry of catenary structures—where nests appear as localized asymmetries—the Symmetry-Aware Railway Nest Detection Framework (RNDF) is proposed, an enhanced YOLOv12 system for accurate and robust nest detection in symmetric high-speed railway catenary environments. With the A2C2f_HRAMi design, the RNDF learns from multi-level features by unifying residual and hierarchical attention strategies. The SCSA component boosts the recognition in visually cluttered or obstructed settings further by jointly processing spatial and channel-wise signals. To sharpen the detection accuracy, particularly for subtle, hidden nests, the Focaler-GIoU loss guides bounding box optimization. Comparative studies show that the RNDF consistently outperforms recent detectors, surpassing the YOLOv12 baseline by 5.95% mAP@0.5 and 26.16% mAP@0.5:0.95, underscoring its suitability for symmetry-aware, real-world catenary anomaly monitoring. Full article

This study investigates the effect of seasonal conductor sag on electromagnetic field (EMF) exposure to near 400 kV double-bundle overhead transmission lines. The conductor sag study resulted in clearance values of 28.0 m for winter (−10 °C, sag ≈ 7.0 m) and 23.4 m for summer (+35 °C, sag ≈ 11.65 m). For both seasonal examples, the electric field strength and magnetic flux density were calculated at a pedestrian height of 1.5 m, and the image approach to account for ground effects. The winter setup resulted in maximum values of 1.35 kV/m (E) and 27.2 µT (B), while the summer configuration produced higher values of 1.96 kV/m and 38.5 µT, respectively. Autumn field measurements, representing intermediate seasonal circumstances, produced average values of 1.294 kV/m and 1.399 µT, with peaks of 8.39 kV/m and 6.85 µT for electric field and magnetic flux density, respectively. The electric field projections were nearly identical to measurements; however, the magnetic field predictions were significantly higher, most likely due to the model’s assumptions of balanced currents and ideal geometry. These findings suggest that seasonal conductor sag variation is a real and substantial factor in assessing EMF exposure, with the electric field being particularly sensitive to clearance changes. The findings emphasize the need to incorporate a large analysis into EMF compliance assessments, especially in cases where terrain relief between towers may further diminish clearance in mid-span regions. Full article

Fatigue failure of overhead line conductors made of AlMgSi alloys is much more complex than fatigue failure of a single wire. The main difference lies in the fretting phenomenon, which is a significant mechanism initiating fatigue damage. It is generated because of micro-movements between individual wires or outer wires and overhead line fittings. Such movements are mainly caused by aeolian vibrations, which lead to degradation of wire surface, initiation of microcracks, and premature failure of multiple wires. Research based on laboratory experiments and modeling studies simulating real operating conditions made it possible not only to identify the mechanisms leading to failure but also to assess the impact of working conditions on their evolution. According to the obtained results, properly selected heat treatment parameters influence both the mass decrease of the wires and number of cycles to failure due to fretting fatigue. Further development of materials, protective coatings, and methods of durability prediction will reduce the impact of fretting on fatigue failure and thus increase the reliability of power lines. Full article

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

Dynamic line rating (DLR) is an effective technique for real-time assessments on current-carrying capacities of overhead lines (OHLs), improving efficiencies and preventing overloads of transmission networks. Most research related to DLR forecasting mainly translates predictions of weather conditions into DLR forecasts or directly trains artificial intelligence models from DLR observations. Less attention has been given to the predictability of effective wind speeds (EWS) that describe overall convective cooling effects of varying weather conditions along OHLs, which could increase the reliability of DLR forecasts. To assess the effectiveness of EWS concepts in improving DLR predictions, this paper develops an EWS-based method for convective cooling predictions which are critical parameters dominating DLRs of overhead conductors. The EWS is first calculated from actual measurements of wind speeds and directions relative to OHL orientation based on the thermal model of overhead conductors. Then, an autoregressive model along with the Fourier series is employed to predict ultra-short-term EWS variations for up to three 10-min steps ahead, which are eventually converted into predictions of convective cooling effects along OHLs. The proposed EWS-based method is tested based on wind condition measurements in proximity to an OHL. Furthermore, to examine the impacts of angles between wind directions and line orientation on EWS estimation and thus EWS-based convective cooling predictions, the forecasting performance is assessed in the context of different line orientations. Results demonstrate that EWS-based ultra-short-term convective cooling predictions consistently outperform traditional forecasts from original wind conditions across all the tested line orientations. This highlights the significance of the EWS concept in reducing the complexity of DLR forecasting caused by the circular nature of wind directions, and in enhancing the accuracy of convective cooling predictions. Full article

Aiming at the ice coating problem on medium-voltage overhead lines, this paper proposes a resonance de-icing device based on an improved YOLOv7 algorithm to achieve efficient and intelligent ice detection and removal. First, by introducing the SimAM three-dimensional attention mechanism to optimize feature extraction capability, combining the MPDIoU loss function to enhance bounding box regression accuracy, and designing a task-specific context decoupling head to separate classification and regression tasks, the ice detection accuracy and real-time performance are significantly improved. Second, an integrated ice observation/de-icing device is developed, which incorporates the improved YOLOv7 visual detection algorithm and a resonance vibration module. Through a dynamic frequency optimization strategy, precise matching between the excitation frequency and the inherent frequency of the conductor is achieved. The findings from the engineering experiments demonstrate that the de-icing apparatus conceptualized in this study is capable of effectively identifying the condition of ice-covered conductors and de-icing them. This research presents a novel technical solution for the intelligent de-icing of overhead lines, which holds significant value for engineering applications. Full article

The development and utilization of large-scale offshore wind power (OWP) are critical measures for achieving global energy transition. To address the demands of future large-scale OWP centralized development and transmission, this study systematically investigates the influencing factors and construction principles for topology selection in offshore wind power high-voltage direct current (HVDC) transmission systems delivering power to load centers. First, under the context of expanding the offshore wind power transmission scale, the necessity of transmitting OWP via HVDC overhead lines directly to load centers after landing is theoretically discussed. Five key topological influencing factors are then analyzed: offshore wind power collection schemes, multi-terminal HVDC network configurations, DC fault isolation mechanisms, offshore converter station architectures, and voltage source converter HVDC (VSC-HVDC) receiving terminal landing modes. Corresponding topology construction principles for direct HVDC transmission to load centers are proposed to guide system design. Finally, the feasibility of the proposed principles is validated through a case study of a multi-terminal HVDC system integrated into an actual regional power grid, demonstrating practical applicability. Full article