Search Results (204)

Non-contact magnetic sensing technology is widely adopted in transmission line online monitoring scenarios including current measurement and fault location for its non-contact measurement capability, strong environmental robustness and low deployment cost. However, existing magnetic-sensing-based galloping monitoring methods suffer from two critical limitations: no theoretical guidance is provided for sensor placement, and a high false detection rate is observed under current fluctuation conditions. To address these issues, a novel transmission line galloping monitoring method based on spatial magnetic field distribution features is proposed in this paper. A conductor galloping-power frequency magnetic field coupling model is first established to derive the optimal magnetic sensor array arrangement strategy. Subsequently, a galloping detection algorithm fusing multi-node frequency-domain features and phase difference information is proposed to eliminate current fluctuation induced false detection. Simulations conducted based on actual 500 kV transmission line parameters and verification tests carried out on a scaled-down laboratory platform confirm that reliable galloping detection can be realized by the proposed method under both current low-frequency oscillation and random fluctuation scenarios. With advantages of non-contact deployment, high anti-interference performance and detection accuracy, the proposed method has promising application potential in engineering-oriented high-voltage transmission line monitoring. Full article

The essential role of electricity supply for public and private services highlights the need to monitor the stability of power transmission networks during, or immediately after, hazardous events. In the aftermath of calamities, traditional field inspections may be impractical or unsafe, leaving operators without timely information on the condition of critical assets. In this paper, we present and discuss the performance of two automatic Artificial Intelligence (AI)-based models (Multi-Layer Perceptron (MLP) neural network architectures and Support Vector Machine (SVM) model) designed to automatically assess the status of high-voltage transmission towers and power lines through multi-temporal spaceborne Synthetic Aperture Radar (SAR) image analysis. Model development and testing rely on real COSMO-SkyMed Stripmap observations of damaged towers and power lines affected by documented hazardous events across Italy, complemented by simulated tower data generated with a physics-guided, signature-based SAR simulator designed to preserve the observed target-to-background contrast and spatial footprint patterns of real SAR tower signatures. Results indicate that the MLP, trained on either real or simulated data, achieved 100% Overall Accuracy (OA) with no observed false positives or false negatives within the considered visibility-screened real test set, while providing inference times on the order of tenths of milliseconds per target… Computational performance characteristics, operational advantages, and the potential pathway toward satellite on-board porting are discussed to enhance situational awareness and support the prioritisation of interventions during critical events. Full article

Hybrid transmission corridors combining overhead lines and underground cables introduce impedance discontinuities that significantly modify electromagnetic transient behavior. These discontinuities generate traveling-wave reflections, waveform distortions, and high-frequency components at relay measurement locations during the first microseconds following disturbance inception. This paper presents a waveform-level electromagnetic transient (EMT) analysis of overhead–cable transition effects using detailed EMTP-RV simulations including frequency-dependent line and cable models, tower representations, grounding systems, and instrument transformers within a differential protection measurement framework. The results show that overhead–cable transitions produce transient waveform modifications characterized by reflections, attenuation, dispersion, and temporary current imbalance mechanisms associated with traveling-wave propagation and cable capacitive effects. The analysis also demonstrates the transient evolution of instantaneous waveform-derived (EMT-derived) differential and restraining current quantities, defined as combinations of terminal current signals obtained directly from EMT waveforms. These quantities do not represent final phasor-domain operating values of practical numerical relays, but provide insight into the transient electromagnetic environment preceding conventional filtering and phasor estimation. The study contributes to a clearer physical interpretation of transient phenomena in hybrid transmission systems and supports EMT-based evaluation of signals relevant to differential protection applications. Full article

Contact sensor-based stress monitoring of transmission towers under strong winds is often limited by complex installation and maintenance procedures and the risk of local structural damage. With the development of non-contact displacement monitoring technologies, such as laser measurement and machine vision, this study proposes a wind-induced stress surrogate model for transmission towers using MISSA-BPNN, aiming to rapidly calculate stresses in vulnerable regions from macroscopic displacement responses. First, finite element analysis is conducted to investigate the wind-induced responses of a tower-line system under different operating conditions, identify vulnerable regions, and construct a dataset using displacement and stress responses. Then, a multi-strategy improved sparrow search algorithm (MISSA) is developed to optimize the initial weights and biases of the BP neural network, thereby establishing the MISSA-BPNN model. The constructed dataset is used to train the model and build the wind-induced stress surrogate model. Results show that the vulnerable regions are mainly located at the leeward tower foot and the middle-lower tower body. Compared with the conventional BPNN model, the proposed MISSA-BPNN surrogate model reduces the MAE, RMSE, and MAPE by 22.43–24.33%. This method provides a new approach for health monitoring of transmission lines under strong winds. Full article

Transmission tower-line systems spanning active faults are simultaneously subjected to the “dual characteristic seismic actions” of permanent ground displacement (PGD) and spatially varying near-fault ground motions, rendering their failure mechanisms far more complex than those under conventional site-specific seismic actions. This paper investigates a 500 kV double-circuit “two-tower, three-line” coupled system by establishing a high-fidelity finite element model. An analytical framework is proposed, centered on indexing seismic action and structural response by key parameters: “Permanent Ground Displacement–Peak Differential Displacement–Velocity Pulse Period” (“PGD–Δmax–Tp”). By employing synthesized ground motions, the displacement time history is decomposed into three components—a velocity pulse, high-frequency background noise, and permanent displacement—thereby achieving a strict decoupling of these three control variables. Based on this methodology, three sets of controlled-variable scenarios were constructed to systematically reveal the independent influence of ground motion spectral characteristics, permanent displacement, and peak differential displacement on the system’s response. The research indicates that: spectral characteristics modulate the failure mode (the whiplash effect is triggered when the period ratio μ is approximately 1–2, whereas tower leg buckling occurs when μ ≫ 1); a threshold PGD value exists that triggers a shift in the structural force-resisting mechanism; and the peak differential displacement (Δmax) causes the system’s response to transition from being dominated by conductor slackening and unloading to being governed by inertia and P-Δ effects. The insights gained into the asymmetric response characteristics of towers on opposite sides of the fault provide a quantitative reference for the revision of seismic design codes for cross-fault power transmission projects. Full article

The planning and design of Extra-High Voltage (EHV) overhead power lines require strict adherence to electromagnetic field exposure limits to ensure public safety. This paper presents a comprehensive analysis of the minimum ground clearance required for standard 400 kV transmission towers to comply with international safety guidelines. A review of legislative frameworks across 37 countries indicates a widespread consensus on limiting values of 5 kV/m for the electric field and 100 $\mathsf{\mu }$T for magnetic flux density. Using analytical methods, the electric and magnetic fields were calculated for four common tower geometries (Cat, Portal, Danube, and Barrel) under varying ground clearances and phase configurations. The results demonstrate that the magnetic flux density is not a limiting factor, as it remains well below safety thresholds even at standard technical clearances. Conversely, the electric field intensity proves to be the critical design constraint, often requiring clearances significantly higher than those dictated by insulation coordination. The study identifies that optimizing the phase sequence in double-circuit towers can reduce the required ground clearance by up to 28%, offering a cost-effective mitigation strategy. These findings provide power line designers with essential decision-making data for the preliminary design phase, enabling the optimization of tower geometry and phase arrangement without the need for computationally intensive simulations. Full article

High-voltage direct current (HVDC) transmission systems are increasingly used for long-distance power transmission and the integration of renewable energy sources. In such systems, outdoor insulators are exposed to combined electrical stresses, including steady DC voltage, transient overvoltages, and environmental contamination, which can significantly influence leakage current behaviour and insulation performance. This work presents an experimental and numerical investigation of leakage currents on artificially contaminated polymer insulators under two application-relevant HVDC operating scenarios. The first scenario considers superimposed HVDC voltage with switching impulses and very slow front overvoltages, which may occur during fault conditions in converter-based HVDC systems. The second scenario investigates electromagnetic coupling effects in a hybrid HVDC/HVAC transmission line configuration, where AC and DC conductors are installed on the same tower. Artificial contamination layers with different morphologies and conductivities are applied to the insulator surface to reproduce realistic pollution conditions. Leakage currents are measured using a high-resolution acquisition system, and the results are supported with numerical simulations based on finite-element modelling. The results show that transient overvoltages significantly increase leakage current amplitude and duration, leading to increased electrical stress on contaminated insulators. In the hybrid transmission configuration, electromagnetic coupling between AC and DC paths induces additional current components in the DC leakage current. The presented results contribute to a better understanding of leakage current behaviour under realistic HVDC operating conditions and provide useful information for insulation assessment and condition monitoring of outdoor insulators in modern HVDC transmission systems. Full article

Precise segmentation of transmission equipment is crucial for ensuring secure power grid operation, yet practical deployment faces substantial challenges including the preservation of elongated morphological characteristics of transmission lines and accurate boundary localization for complex transmission tower structures. This paper proposes a novel segmentation method that synergistically integrates cross-directional convolutions with multi-layer attention mechanisms within the YOLO11 framework. The designed C3x cross-directional convolution module incorporates orthogonal convolutional operations during feature extraction, enabling independent enhancement of feature responses along horizontal and vertical dimensions. This architecture effectively captures continuous morphological characteristics of elongated targets while mitigating fragmentation artifacts. Additionally, the proposed Multi-Layer Cascaded Attention (MLCA) module employs a progressive fusion strategy combining spatial and channel attention, significantly augmenting the network’s capacity to extract multi-scale semantic information while maintaining computational efficiency. This design particularly enhances boundary detail preservation for structurally complex targets. Experimental evaluations on the TTPLA dataset (comprising 1232 images across 4 categories) demonstrate remarkable performance improvements: bounding box detection achieves 72.56% mAP@0.5 and mask segmentation reaches 68.37% mAP@0.5, representing gains of 2.97% and 4.52% respectively over the baseline YOLO11 model. The Mask F1 score improves from 67.85% to 71.76%, comprehensively validating the proposed method’s effectiveness in enhancing segmentation capabilities for both elongated and morphologically complex targets. These results substantiate the practical applicability of the proposed approach for intelligent transmission infrastructure monitoring systems. Full article

To accurately evaluate the electric and magnetic field distribution characteristics around transmission lines under different tower structures and operating conditions, this study systematically investigates the spatial electric and magnetic fields of transmission line towers based on Grid Information Model (GIM) file parsing and finite element simulation. First, key information, including tower geometric configuration, conductor suspension point locations, and voltage level, is extracted by parsing the GIM file. A unified transformation method from geographic coordinates to three-dimensional Cartesian coordinates is established, and a three-dimensional electric and magnetic field calculation model is constructed in the ANSYS Maxwell platform, incorporating a catenary conductor model and an equivalent representation of bundled conductors. Furthermore, the accuracy of the proposed calculation method is validated based on field measurement data. Second, under single-circuit operating conditions, the spatial electric and magnetic field distributions of the Goblet-shaped suspension tower and the Drum-type transmission tower are analyzed under different phase sequence arrangements and different conductor-to-ground heights, and the shielding effect of the tower structure on the local electric field is investigated. On this basis, an electric field fitting method based on a proportional polynomial model is proposed, enabling the prediction of electric field distribution under tower-present conditions using simulation results obtained without tower structures. Subsequently, the influence of different phase sequence combinations on the spatial electric field distribution is systematically examined. The fitting method is further extended to double-circuit transmission lines, and its accuracy and effectiveness in rapid electric field assessment are verified. Finally, from an engineering practice perspective, the effects of the presence of jumper conductors and variations in conductor turning angles on the spatial electric field distribution of double-circuit towers are analyzed, and an optimized estimation approach for electric fields under different turning angle conditions is proposed. The results demonstrate that tower structural configuration and conductor arrangement significantly affect the electric field distribution, and the proposed fitting method effectively reduces modeling complexity while maintaining computational accuracy. The findings of this study provide a theoretical basis and technical reference for electric and magnetic environment assessment and engineering design of transmission lines. Full article

With the increasing density of transmission lines, line crossings and spans have become more common, and the electromagnetic environment of transmission lines has attracted increasing attention. Investigating the electromagnetic field distribution in transmission line crossing regions is therefore of great significance for line layout and preliminary design. In this study, the parameters of transmission lines in crossing regions are first obtained by parsing the GIM (Grid Information Model) file. A three-dimensional electromagnetic field model of a double-circuit transmission line on the same tower is then established using the finite element method, and the accuracy of the proposed approach is validated by comparison with field measurement data. Based on the developed model, the electric and magnetic field distributions of both the double-circuit transmission line and the crossing region are calculated. Furthermore, the effects of different crossing angles, phase sequence combinations, and voltage levels on the electromagnetic field distribution are systematically investigated. By comparing the electromagnetic field characteristics under different phase sequence schemes, an optimized phase sequence configuration for double-circuit transmission lines and crossing regions is proposed. The results provide a theoretical basis and technical reference for electromagnetic environment assessment and design optimization of transmission lines in crossing regions. Full article

Life cycle greenhouse gas (GHG) accounting is increasingly required to substantiate the climate value of wind power beyond “zero-emission” operation, especially under China’s dual-carbon targets. Robust estimation of life cycle GHG emission intensity and the identification of actionable mitigation levers are therefore important for credible transition planning. In this study, a process-based life cycle assessment (LCA) was conducted for a representative 100 MW onshore wind farm in Gaoyou, Jiangsu Province, China, following ISO 14040/14044. To enhance engineering relevance, the construction and installation phase was modeled in a refined manner by decomposing it into road, wind-turbine, booster-station, and transmission-line engineering and further into unit processes. The results show that the overall life cycle GHG emission intensity of the studied wind farm is 24.6 g CO₂-eq/kWh. Scenario analysis further indicates that reducing curtailment and improving end-of-life recycling are effective pathways to lower emission intensity, while the net advantage of hybrid versus steel towers depends on recycling performance when end-of-life credits are included. The study also summarizes practical implications for low-carbon equipment/material procurement and green supply-chain governance, low-carbon construction and logistics, coordinated “source–grid–load–storage” planning to curb curtailment, and more standardized and comparable life cycle carbon accounting for wind projects in China. Full article

To address the low efficiency of finite element modeling and the reliance on manual measurements in electric/magnetic field analysis of complex overhead transmission line structures, this paper develops an IronPython-based automated computational platform within ANSYS Maxwell for 3-D modeling and electric/magnetic field analysis. First, by parsing transmission line data from the Grid Information Model (GIM), a unified coordinate transformation method is proposed to convert geographical coordinates into three-dimensional (3-D) Cartesian coordinates for finite element analysis. Based on the extracted line parameters, conductor sag is calculated and catenary modeling is implemented. An equivalent radius method is also introduced to simplify multi-bundle conductor modeling, enabling fast parametric construction of complex 3-D transmission line models. Second, by combining the IronPython scripting language with the .NET Windows Forms control library, a visualized finite element modeling and computation platform is developed. Finally, a typical double-circuit transmission line on the same tower is taken as a case study to calculate the spatial distribution of electric/magnetic fields. The influence of solution domain size on electric/magnetic field computation results is investigated, and optimal solution domain parameters are determined. The finite element results generated by the developed platform are further validated through comparison with measured data. The results demonstrate good agreement between calculated and measured values, confirming the accuracy and engineering applicability of the developed platform for electric/magnetic environment analysis of overhead transmission lines. Full article

Low solution accuracy and efficiency are two bottleneck problems in the existing models and methodologies for spatial distance calculations to verify the minimal electrical clearance of overhead transmission lines if a dynamic windage yaw is considered. To address these two issues, the accurate numerical models and the corresponding efficient solution methodologies tailored for different scenarios are proposed. First, a conductor windage yaw surface model incorporating a horizontal specific load coefficient is established, transforming the wire-to-wire minimal distance determination into a multi-dimensional nonlinear constrained optimization problem. An improved gradient-guided crossover genetic algorithm (GGA) is subsequently developed to solve this optimization problem. By integrating the gradient information to guide the crossover operator and combining an adaptive mutation with a dimension mutation strategy, the solution efficiency is enhanced. For the wire-to-tower minimal distance determination, a simplified tower model and a hybrid optimization methodology combining an oriented octree with the GGA are proposed. Numerical results on typical case studies show that, for a wire-to-wire minimal distance calculation, the GGA outperforms both the basic genetic algorithm and particle swarm optimization in terms of both convergence speed and solution accuracy. For a wire-to-tower minimal distance calculation, the oriented octree improves the spatial utilization, and the proposed hybrid methodology substantially improves the computational performance. Full article

The paper presents a comprehensive deep learning-based framework for automated visual inspection of overhead power line infrastructure using unmanned aerial vehicles. Traditional manual and helicopter inspections are costly, time-consuming, and hazardous for maintenance personnel. The proposed approach integrates UAV imaging with advanced computer vision models such as YOLOv8, EfficientDet-D2, and Faster R-CNN to automatically detect defects in critical components, including insulators, conductors, and transmission towers. Several open datasets (InsPLAD, TTPLA, MPID) were used for training and validation, ensuring robustness under diverse lighting and environmental conditions. Experimental results demonstrate that YOLOv8 achieved the best performance, reaching 88.5% mAP@0.5 with real-time inference capabilities (over 50 FPS on GPU). The system significantly enhances inspection efficiency, allowing for a threefold increase in coverage capacity and an up to 70% reduction in defect remediation time. The integration of AI-powered visual analytics with maintenance and SCADA systems enables a shift from reactive to predictive maintenance, improving the safety, reliability, and resilience of power transmission infrastructure. Full article

Escalating global climate change has intensified the frequency and scale of wildfires in mountainous regions hosting transmission line infrastructure. These conflagrations act as extreme meteorological events, capable of generating localized heatwaves that compromise the air insulation of power lines and trigger protective relay operations, thereby posing systemic threats to regional grid stability. To enhance wildfire early-warning efficacy for grid security, this study formulates wildfire early warning for power transmission corridors as a regression-based risk prediction problem and proposes a hierarchical “global screening–local refinement” risk assessment framework. The primary contribution of this study lies in the integration of a machine-learning-based global wildfire risk screening model with tower-level spatial refinement using geographically weighted regression (GWR), enabling coordinated global–local wildfire risk characterization along power transmission corridors The framework employs a predictive model built on a Gradient Boosting Decision Tree algorithm, integrating geospatial and statistical analyses. A global risk model, utilizing historical data from the Himawari-8 satellite alongside meteorological, topographic, and anthropogenic variables, produces a composite risk index. This index is spatially interpolated via Kriging to generate stratified wildfire risk maps for broad-area assessment. For precise corridor-level analysis, these Globally Projected Risk Indices, along with localized terrain features, inter-tower clearance distances, and proximity to historical ignition points, are incorporated into a Geographically Weighted Regression model. This yields a spatially calibrated wildfire risk index along critical routes. The results show that the GBDT-based model achieved the best predictive performance among the evaluated regression models, with an R² of 0.626 and a mean squared error of 0.178. This approach offers a scientifically robust and operationally viable reference for wildfire prevention strategies in power line maintenance. Full article