Search Results (669)

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

Geometric monitoring of slender support structures, particularly steel lattice transmission towers, is a critical component of power infrastructure diagnostics. Such structures are susceptible to environmental influences and long-term deformation processes, which necessitates precise assessment of their geometric axis. The aim of this study was to evaluate the influence of the terrestrial laser scanning (TLS) scanner class and point cloud registration strategy on the determination of the geometric axis of a steel high-voltage lattice transmission tower (hereafter LTT). Unlike previous studies focused primarily on TLS-based axis reconstruction, this work introduces a comparative assessment of registration strategies, an error propagation model, and the proposed Axis Drift Index (ADI) as quantitative indicators of axis stability. The analysis was based on data obtained using a tachymetric method (reference), a compact scanner (Leica BLK360), and a survey-grade scanner (Riegl VZ-400i). The comparison included planimetric axis deviation, consistency of deformation direction, variation in results with height, and the influence of registration quality. The results show that TLS measurements performed using a survey-grade scanner and target-based registration exhibit high agreement with tachymetric results. In contrast, cloud-to-cloud registration without a stable reference framework leads to cumulative errors and instability of the reconstructed axis, particularly in the upper parts of the structure. The observed deviations in the BLK360 dataset were dominated by registration-related geometric instability rather than unequivocal structural deformation signals. The findings indicate that the accuracy of geometric axis determination in slender structures is governed more by the adopted point cloud registration strategy than by the scanner class itself. The proposed ADI parameter and linear error propagation model additionally enabled a quantitative assessment of geometric consistency with height. From an engineering perspective, this highlights the importance of stable reference systems and appropriate survey design in high-precision TLS applications. Although the study was conducted on a single lattice tower, the results provide practical insight into the reliability of TLS workflows for slender structures characterized by discontinuous geometry and high sensitivity to registration errors. Full article

Wind turbine towers rely on welded joints for structural continuity, and the coarse-grained heat-affected zone (CGHAZ) at these joints is the principal site of fatigue damage under service loading. This study characterises the influence of welding heat input on the microstructural constitution, high-cycle fatigue response, and fracture mechanisms of Gleeble-simulated CGHAZs in a Nb-microalloyed wind power steel. Thermal cycles representative of submerged arc welding at 15, 25, 35, and 45 kJ/cm were applied, and the resulting microstructures were examined by optical microscopy, SEM, EBSD, and TEM. Raising the heat input produced systematic microstructural coarsening: the densities of low-angle grain boundaries (LAGBs) and high-angle grain boundaries (HAGBs) fell by approximately 40% and 26%, respectively, while the mean equivalent diameter (MED) and prior austenite grain (PAG) size grew by roughly 64% and 67%. Life partitioning showed that crack nucleation accounted for more than 84% of total fatigue cycles in every condition, identifying it as the life-governing damage stage. Over the 15-to-45 kJ/cm range, the CGHAZ fatigue strength at 2 × 10⁶ cycles deteriorated from 246.9 MPa to 208.5 MPa (a 15.6% reduction), while the mean fatigue striation spacing widened from 0.142 μm to 0.183 μm (an increase of 28.9%). These results demonstrate that judicious heat-input selection is a practical and effective means of preserving CGHAZ fatigue integrity in wind tower steel fabrication, and they address a previously unresolved gap concerning high-cycle fatigue fracture mechanisms in this critical microstructural zone. Full article

Permanent magnet synchronous generators (PMSGs) are inevitably subject to aperiodic and periodic disturbances due to complex operating conditions and internal coupling effects. To improve speed regulation under such disturbances, this paper develops a hierarchical control framework that integrates a parameter-decoupled extended state observer (DESO) with quasi-resonant control. A novel parameter decoupling method enables independent tuning of the observer bandwidth and controller parameters, while the quasi-resonant control module specifically targets periodic torque ripples caused by the tower shadow effect. Simulation results under stochastic wind conditions confirm that the proposed QR-DESO significantly outperforms conventional methods, reducing the speed tracking root mean square error (RMSE) by $61.8\%$ and the total harmonic distortion (THD) to $0.17\%$. The system also exhibits strong robustness against $\pm 20\%$ parameter mismatches, validating its effectiveness for offshore wind power applications. Full article

Corrosion-driven section loss in steel tower structures erodes load-carrying capacity, yet field assessment still relies on subjective visual grading. This paper presents a closed-loop framework coupling quantitative image-based corrosion measurement with stochastic degradation modeling, Monte Carlo reliability simulation, and Sobol’ variance-based global sensitivity decomposition. Two complementary segmentation paths—hue–saturation–value (HSV) color-space thresholding for fleet-scale screening and DeepLabV3+ deep learning for detailed evaluation—convert imagery into calibrated section-loss estimates via nonlinear regression. Three analysis modes (single-image, multi-angle weighted-median fusion, and Oriented FAST and Rotated BRIEF (ORB) feature-matched temporal differencing) feed a Bayesian-updated power-law corrosion growth model whose outputs propagate through a time-dependent limit-state function via 10⁶-sample Monte Carlo simulation. Sobol’ indices rank each uncertain input’s contribution to the reliability-index variance. A field demonstration on a 40-year-old galvanized lattice tower in an ISO 9223 C4 coastal environment shows that the corrosion rate constant and zinc coating thickness together govern 65% of the total reliability variance and that a risk-ranked selective maintenance strategy reduces expected life-cycle cost by 71% relative to blanket intervention. Full article

Conventional solar power tower (SPT) systems often suffer from significant heat transfer exergy destruction due to large temperature differences between the heat source and the working fluid during the heat exchange process. To overcome this limitation, a high–low dual-tower configuration based on segmented thermal utilization is proposed. In this arrangement, the high-temperature tower is mainly responsible for the evaporation, superheating, and reheating processes, whereas the low-temperature tower primarily handles feedwater preheating. Such a configuration improves the temperature matching characteristics during the heat exchange process. A comprehensive model integrating the heliostat field, receiver, thermal energy storage system, and power block was developed and validated against Solar Two experimental data, showing good agreement. Comparative analyses were conducted under identical solar resource and operating conditions. The results indicate that the proposed system achieves a comparable power output while reducing total heat transfer exergy destruction by approximately 24%, with a significant reduction of over 80% in the preheating section. Sensitivity analysis further reveals that optimizing the high tower outlet temperature can effectively reduce irreversibility and slightly enhance power output, although constrained by the pinch temperature difference. Dynamic simulations based on typical meteorological year data demonstrate that the system maintains stable operation and improves cycle efficiency. From an economic perspective, the proposed system reduces the levelized cost of electricity (LCOE) by about 6.6% and shortens the dynamic payback period, indicating enhanced long-term competitiveness. Overall, the high and low dual-tower system effectively improves thermodynamic and economic performance, providing a promising approach for high-efficiency concentrating solar power (CSP) development. Full article

Automated power line inspection plays a crucial role in maintaining grid reliability within smart cities by identifying potential defects in towers, conductors, insulators, and fittings. While modern anomaly detection relies heavily on deep neural networks (DNNs), training these models requires massive amounts of high-quality image data. However, a significant scarcity of publicly available datasets persists because data acquisition not only demands highly specialized professional skills but also faces strict data protection regulations enforced by grid companies. To bridge this gap, this paper presents a comprehensive review of open-access image datasets dedicated to power line inspection. Based on strict inclusion criteria—specifically, unrestricted public availability and a direct focus on core power line components—19 datasets are systematically selected and analyzed. We provide a detailed taxonomy and comparative analysis of these datasets in terms of inspection targets, acquisition platforms, annotation toolkits, and labeling schemes. Furthermore, our investigation highlights current research trends and identifies critical gaps, such as the disproportionate focus on insulators and the notable scarcity of multimodal data. To address the limitations of small-scale datasets, we also discuss existing data augmentation strategies and synthetic data generation techniques. Ultimately, this review serves as a unified navigational guide, aiming to foster the development of more robust visual inspection algorithms and to inspire future high-quality dataset construction in the power domain. Full article

While conventional wind towers operate at heights of 80 to 90 m across many regions, including the United States, emerging tower technologies enable higher hub heights that are expected to reduce the levelized cost of energy (LCOE) and increase profit margins. This paper investigates whether increased hub heights, as well as different turbine technologies, deliver measurable economic and performance benefits in wind-rich regions using measured and simulated wind data. First, a model for estimating hourly and monthly energy production is validated with data from a site in Minnesota. To evaluate the advantages of tall towers, the model is extended to estimate the annual energy production (AEP) at various hub heights across multiple sites using different wind datasets. The results confirm that simulated data can be effectively used for predicting AEP and capacity factors in wind-rich regions. Next, it is demonstrated that increasing the hub height by 20 m yielded an average 11% increase in AEP and an 18% reduction in LCOE. Finally, the integration of advanced turbine technologies with taller towers shows the potential to reduce the LCOE of wind power by 23% while increasing profit margins by over 40%. Full article

Wireless power transfer (WPT), characterized by its excellent insulation properties and ease of maintenance, has recently emerged as a promising solution to the power supply challenges faced by online monitoring equipment on high-voltage transmission towers in complex environments. Existing research primarily relies on regular, closely wound solenoids to power these monitoring devices; however, this approach often makes it difficult to optimize the magnetic field distribution to maximize mutual inductance, thereby limiting transmission efficiency and power and hindering lightweight design. To address these issues, this paper proposes an optimized design scheme for variable-pitch (non-uniform) domino WPT coils based on insulator string structures. First, a parameter calculation model utilizing segmented current analysis is constructed to accurately determine the inductance of non-uniform solenoids, with simulations confirming an error rate below 5%. Subsequently, by integrating domino multi-coil theory into an elitist non-dominated sorting genetic algorithm (NSGA-II), dual-objective optimization is performed. Targeting maximum transmission efficiency and output power under spatial and insulation constraints, a set of Pareto optimal solutions is derived. Ultimately, a 113.7 W insulator domino coil WPT system prototype is constructed to validate the design’s stability. The proposed system achieves a maximum efficiency of 85.73%, with a single-stage load delivering up to 97.48 W. Full article

An effective sensor network strategy is fundamental to structural health monitoring (SHM). Optimal sensor placement (OSP) for transmission towers remains insufficiently studied, primarily owing to the extensive number of candidate nodes and the complex structural responses of these structures under diverse environmental loads. Utilizing finite element analysis (FEA), this paper proposes a novel framework for the sensor placement of transmission towers. The maximum modal order of a Y-shaped transmission tower is determined using the Fisher Information Matrix (FIM), which characterizes its dynamic properties, while the Modal Assurance Criterion (MAC) is employed to identify the optimal number of sensors. The Marine Predators Algorithm (MPA) is then utilized to determine the optimal sensor configuration for the transmission tower based on four different fitness functions. The performance of these four fitness functions in sensor layout design is systematically compared. The results indicate that the MPA can efficiently generate optimal sensor configurations under a constraint on the maximum number of sensors. The choice of fitness function has a significant impact on the sensor placement results. The proposed MPA-based OSP method provides a reliable technical framework for the optimal design of SHM systems in power transmission engineering. Full article

Satellite-based assessment of power tower damage is essential for rapid disaster response but is challenged by the scarcity of damage samples and the cross-scale mismatch between close-range UAV imagery and satellite imagery. Existing data augmentation methods, including copy-based strategies and diffusion-based generation, often fail to produce reliable samples due to their dependence on the training data distribution and the lack of explicit control over object scale and domain discrepancy. To address these issues, we propose a scale-constrained and frequency-adaptive diffusion-based data construction framework that explicitly models the scale distribution prior of power towers in the remote sensing domain and incorporates frequency-domain adaptation before image generation. Specifically, scale-aware instance embedding is used to construct training samples that conform to satellite-scale statistics, while frequency-domain adaptation is introduced to reduce spectral and texture discrepancies between UAV-derived damaged references and satellite imagery. A diffusion-based inpainting model is then trained on the constructed dataset to reconstruct damage at original tower locations. The experimental results, including feature statistical analysis and downstream change detection validation, demonstrate that the proposed method achieves better alignment with real satellite-scale distributions, reduces geometric and spectral–textural inconsistencies, and improves boundary continuity and structural realism under cross-resolution conditions. Full article

This paper presents AeroelasticQ, a modular, high-performance aeroelastic simulation code for wind turbines, with particular emphasis on future applicability to multi-rotor configurations. The framework is organized into three core components: a flexible-blade structural solver, an airfoil-based aerodynamic solver, and a two-mesh aero-structural mapping module for transferring loads and kinematics between the aerodynamic and structural discretization. The implementation is written in C++17 using the Eigen linear algebra library (v5.0.0), and OpenMP (v5.1) is employed to enable rotor-level parallel execution for multi-rotor applications. The structural dynamics are formulated using Kane’s dynamic method combined with modal superposition, while the aerodynamic loads are computed using three-dimensional blade element momentum theory. The coupled and uncoupled modules are validated in the time domain against OpenFAST (v4.1.2) AeroDyn, ElastoDyn, and the coupled AeroDyn–ElastoDyn configuration using the NREL 5 MW reference wind turbine. The rotor-level aerodynamic validation gives mean absolute errors of 8.94 × 10⁻⁴, 2.82 × 10⁻⁴, and 2.71 × 10⁻⁵ for C_t, C_p, and C_q, respectively, while the coupled aeroelastic cases show close agreement in blade tip deflections, blade root loads, and aerodynamic power. A rigid three-rotor verification confirms the multi-rotor load-aggregation framework, with tower base thrust and overturning moment errors below 1.5% and 2% NRMSE, respectively, in both all rotors operating and one operating/two-parked configurations. In single-thread benchmarks, AeroelasticQ achieves speedups of 5.23×, 19.69×, and 3.65× in the aerodynamic-only, structural-only, and fully coupled modes, respectively. In the multi-rotor benchmark, the five-rotor case achieves a parallel speedup of 2.55× with a parallel efficiency of 51%. Full article

To mitigate the risks of pressure surges and water hammer during accidental pump trips in industrial cooling water systems, accurate boundary modeling of cooling towers is essential. This study employs the Method of Characteristics (MOC) to evaluate four equivalent models for the central riser shaft: Model A (constant level), Model B (two-way surge tank), Model C (dynamic coupling of shaft and distribution channel), and Model D (composite structure). Results indicate that Model A fails to reflect actual hydraulic states, producing an unrealistic pump reverse speed of −253.24 r/min and overly conservative estimates. While Models B, C, and D exhibit similar pressure trends, Model C most accurately captures the physical drainage process, realistically simulating how the shaft level stabilizes at the distribution channel elevation before declining. By accurately reflecting engineering hydraulics, Model C provides the most reliable basis for water hammer safety assessments. It is recommended for optimizing pump valve closure strategies, vacuum breaker installations, and siphon protection designs in power plant systems. 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

To address the high physical demands faced by personnel engaged in power maintenance operations, this study develops a hip assistive exoskeleton capable of state recognition between level-ground walking and transmission tower climbing. The mechanical structure of the exoskeleton is designed based on motion data analysis of human level-ground walking and tower climbing activities. A dynamic model of the human lower limb is conducted to support state-based torque control of the actuators. To accommodate different locomotion scenarios, a control strategy based on a hierarchical finite state machine (HFSM) is proposed to achieve adaptive state recognition and enable the exoskeleton to provide state-specific torque output. State recognition and transition experiments, alongside laboratory and field transmission tower climbing experiments, are conducted. The results show that the exoskeleton can reliably recognize transitions between walking and climbing, providing effective assistance during transmission tower climbing operations. Furthermore, laboratory and field transmission tower climbing experiments show that exoskeleton assistance reduces integrated EMG (IEMG), root mean square (RMS) and maximum absolute value (MAXABS) values of the biceps femoris (BF), rectus femoris (RF), and vastus medialis (VM), demonstrating the effectiveness of the exoskeleton. Full article