Search Results (311)

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The approach involves calculating the CorV of structural members before and after damage using dynamic response data, employing the CorV assurance criterion (CVAC) to quantify changes in CorV, and introducing first-order differencing for damage localization. Taking an actual transmission tower in Jiangmen as the engineering backdrop, a finite element model is established. Damage conditions are simulated by reducing the stiffness of specific members, and parameter analyses are conducted to validate the proposed method. Furthermore, experimental validation in a lab is performed to provide additional confirmation. The results indicate that the CVAC value of the damaged structure is significantly lower than that in the healthy state. By analyzing the relative changes in the components of CorV, the damage location can be accurately determined. Notably, this method only requires acquiring the time-domain response signals of the transmission tower under random excitation to detect both the existence and location of damage. Consequently, it is well suited for structural health monitoring of transmission towers under environmental excitation. Full article

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

In high-voltage transmission lines, insulators subjected to prolonged electromechanical stress are prone to zero-value defects, leading to insulation failure and posing significant risks to power grid reliability. The conventional detection method of spark gap is vulnerable to environmental interference, while the emerging electric field distribution-based techniques require complex instrumentation, limiting its applications in scenes of complex structures and atop tower climbing. To address these challenges, this study proposes an electroluminescent sensing strategy for zero-value insulator identification based on the electroluminescence of ZnS:Cu. Based on the stimulation of electrical stress, real-time monitoring of the health status of insulators was achieved by applying the composite of epoxy and ZnS:Cu onto the connection area between the insulator steel cap and the shed. Experimental results demonstrate that healthy insulators exhibit characteristic luminescence, whereas zero-value insulators show no luminescence due to a reduced drop in electrical potential. Compared with conventional detection methods requiring access of electric signals, such non-contact optical detection method offers high fault-recognition accuracy and real-time response capability within milliseconds. This work establishes a novel intelligent sensing paradigm for visualized condition monitoring of electrical equipment, demonstrating significant potential for fault diagnosis in advanced power systems. Full article

Due to the coupling of DC and AC components, the ion flow field of HVDC and HVAC transmission lines in the same corridor or even the same tower is complex and time-dependent. In order to effectively analyze the ground-level electric field of hybrid transmission lines, the Krylov subspace methods with pre-conditioning treatment are used to solve the discretization equations. By optimizing the coefficient matrix, the calculation efficiency of the iterative process of the electric field in the time domain is greatly increased. Based on the limit of electric field, radio interference and audible noise applied in China, the main factor influencing the design of hybrid transmission lines is determined in terms of electromagnetic environment. After the ground-level electric field of transmission lines with different configurations is analyzed, the minimum height and corridor width of double-circuit 500 kV HVAC lines and one-circuit ±800 kV HVDC lines in the same corridor are obtained. The research provides valuable practical recommendations for optimal tower configurations, minimum heights, and corridor widths under various electromagnetic constraints. Full article

Under the background of continuous breakthroughs in the spatial resolution of satellite remote sensing technology, high-resolution remote sensing images have become a frontier data source for intelligent inspection research of power infrastructure. To address existing issues in remote sensing image application algorithms such as difficulties in power target feature extraction, low detection accuracy, and false positives/missed detections, this paper proposes the YOLOv9-GDV power tower detection algorithm specifically for power tower detection in high-resolution satellite remote sensing images. Firstly, under high-resolution imaging conditions where transmission tower features are prominent, a Global Pyramid Attention (GPA) mechanism is proposed. This mechanism enhances global representation capabilities, enabling the model to better understand object–background relationships and effectively integrate multi-scale spatial information, thereby improving detection accuracy and robustness. Secondly, a Diverse Branch Block (DBB) is embedded in the feature extraction–fusion module, which enriches the feature space by enhancing the representation capability of single-convolution operations, thereby improving model feature extraction performance without increasing inference time costs. Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. This method employs variable input parameters to directly calculate key point distances between predicted and ground-truth boxes, more accurately reflecting positional differences between detection results and reference targets, thus effectively improving the model’s mean Average Precision (mAP). On the Satellite Remote Sensing Power Tower Dataset (SRSPTD), the YOLOv9-GDV algorithm achieves an mAP of 80.2%, representing a 4.7% improvement over the baseline algorithm. On the multi-scene high-resolution power transmission tower dataset (GFTD), the algorithm obtains an mAP of 94.6%, showing a 2.3% improvement over the original model. The significant mAP improvements on both datasets validate the effectiveness and feasibility of the proposed method. Full article

Thorough investigation into dune morphology is pivotal for grasping the intricacies of constructing and operating power transmission lines in desert terrains. However, there remains a notable gap in the quantitative analysis and assessment of how dune dynamics evolve under the influence of transmission infrastructure. In this study, the Real-Space Cellular Automaton Laboratory is deployed to explore how transverse dunes evolve around transmission towers under diverse wind velocities and varying dune dimensions. The results reveal that, beyond the immediate vicinity of the transmission tower, the height of the transverse dune remains largely stable across broad spatial scales, unaffected by the transmission line. As wind velocities wane, the structural integrity of the transverse dunes is compromised, leading to an expansion in the size of the trail structures. Initially, the height of the dune surges, only to decline progressively over time, with the maximum fluctuation reaching nearly $1\mathrm{m}$. The height of larger dunes escalates gradually at first, peaks, and then subsides, with the pinnacle height nearing $6.5\mathrm{m}$. As a critical metric for safety evaluation, the height of the transmission line above ground initially plummets, then gradually rebounds, and shifts backward over time after hitting its nadir. Full article

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. This work proposes and demonstrates a methodology linking volunteered geographic information from OpenStreetMap (OSM), street-level imagery from Google Street View (GSV), and deep learning object detection models into the automated creation of exposure datasets for power grid transmission towers, assets particularly vulnerable to strong wind, and other perils. Specifically, the methodology is implemented through a start-to-end pipeline that starts from the locations of transmission towers derived from OSM data to obtain GSV images capturing the towers in a given region, based on which their relevant features for risk assessment purposes are determined using two families of object detection models, i.e., single-stage and two-stage detectors. Both models adopted herein, You Only Look Once version 5 (YOLOv5) and Detectron2, achieved high values of mean average precision (mAP) for the identification task (83.67% and 88.64%, respectively), while Detectron2 was found to outperform YOLOv5 for the classification task with a mAP of 64.89% against a 50.62% of the single-stage detector. When applied to a pilot study area in northern Portugal comprising approximately 5.800 towers, the two-stage detector also exhibited higher confidence in its detection on a larger part of the study area, highlighting the potential of the approach for large-scale exposure modeling of transmission towers. Full article

With the extensive construction of high-voltage power grid projects in complex mountainous terrains, rainfall-induced slope instability poses a significant threat to the safety of transmission tower foundations. This study focuses on a power transmission and transformation project in Huizhou City, Guangdong Province. Using MIDAS GTS NX 2019 (v1.2), an unsaturated seepage-mechanics coupling model was established to systematically investigate the influence of slope ratios (1:0.75, 1:1, and 1:1.25) on slope stability under rainfall conditions and the reinforcement effects of anti-slide piles. The results demonstrate that slope ratios significantly govern slope responses. For steep slopes (1:0.75), post-rainfall matrix suction loss reached 43.2%, peak displacement attained 74.49 mm, and the safety factor decreased by 12.5%. In contrast, gentle slopes (1:1.25) exhibited superior stability. Anti-slide piles effectively controlled displacement growth (≤9.15%), but pile bending moments increased markedly with steeper slope ratios, accompanied by a notable expansion of the plastic zone at the slope toe. The study reveals a destabilization mechanism characterized by “seepage–strength degradation–displacement synergy” and recommends engineering practices adopting slope ratios of 1:1–1:1.25, combined with anti-slide piles (spacing ≤ 1.5 m) and dynamic drainage measures. These findings provide critical guidance for the design of transmission tower slopes in mountainous regions. Full article

The development of lightweight de-icing equipment for partial transmission lines in a microtopography area has become a hot research topic. However, the existing local line de-icing methods pay less attention to the mechanical damage caused by unequal tension on the tower, and there is a lack of safe de-icing strategies. This study has proposed a methodology integrating an orthogonal experimental design and finite element mechanical analysis to assess the impact of localized line de-icing on the structural stability of transmission tower-line systems. Taking the ±800 kV transmission line as an example, the refined finite element model of the transmission tower-line system has been established, the influence of each conductor and ground wire defrosting on the tower has been analyzed, and a scientific de-icing strategy has been formulated. Thus, the critical ice thickness and wind speed curves for tower failure have been calculated. The research results show that the de-icing of conductor 1, 5, 6, and ground wires 11 and 12 has a higher impact on the failure of the entire tower-line system. Ice melting on the windward side and ice covering on the leeward side will cause the unbalanced tension of the tower to be greater. The findings provide actionable guidelines for the formulation of a transmission line de-icing strategy and reduce the damage caused by ice. Full article

Accurate identification of surface cracks is of great significance for the site selection of transmission towers, as it directly affects the safety and stability of power grid construction. Traditional manual inspection methods are labor-intensive and inefficient, making them inadequate for large-scale and high-precision applications. Consequently, intelligent crack recognition technologies are receiving increasing attention and adoption. This study proposes a novel surface crack identification method that integrates multiple neural networks, aiming to overcome the limitations of traditional crack identification approaches, such as low accuracy and poor generalization. The proposed framework incorporates a convolutional neural network (CNN)-based data filtering module and a crack segmentation module combining UNet and YOLOv8 architectures. Together, these components form a robust and accurate end-to-end crack identification system, which is further applied to the evaluation of rock fragmentation. To verify the effectiveness and accuracy of the proposed method, experiments were conducted on a rock crack dataset and compared against several existing approaches. The results demonstrate that the proposed method achieves superior performance in crack detection accuracy. Moreover, tests on various scenario datasets also yielded promising results, indicating the strong generalization ability and adaptability of the method. Full article

To investigate the failure evolution and structural response of transmission towers under the combined effects of foundation sliding and wind loads, this study used the foundation sliding incident of Tower No. 39 on the Xiaoxing transmission line as a case for numerical back-analysis. A transmission tower model was first developed based on the finite element method, and the simulation results were compared with field observations to validate the model, with particular focus on the consistency of typical failure modes such as leg bending and cross-bracing instability. On this basis, the structural response under the combined action of foundation lateral displacement, settlement, and wind loads was further simulated. The results indicate that foundation sliding significantly affects the structural stability of transmission towers, with single-foundation sliding being more destructive than the simultaneous sliding of multiple foundations on the same side. Moreover, the coupling of foundation sliding and wind load substantially reduces the critical displacement required to trigger structural failure. Finally, critical displacement thresholds are proposed, which can serve as reference criteria for damage assessment and engineering intervention when changes in foundation conditions occur. Full article

To address the lack of robustness against illumination and blurring variations in aerial images of transmission towers, an improved image matching algorithm for aerial images is proposed. The proposed algorithm consists of two main components: an enhanced AKAZE algorithm and an improved three-stage feature matching strategy, which are used for feature point detection and feature matching, respectively. First, the improved AKAZE enhances image contrast using Contrast-Limited Adaptive Histogram Equalization (CLAHE), which highlights target features and improves robustness against environmental interference. Subsequently, the original AKAZE algorithm is employed to detect feature points and construct binary descriptors. Building upon this, an improved three-stage feature matching strategy is proposed to estimate the geometric transformation between image pairs. Specifically, the strategy begins with initial feature matching using the nearest neighbor ratio (NNR) method, followed by outlier rejection via the Grid-based Motion Statistics (GMS) algorithm. Finally, an improved Random Sample Consensus (RANSAC) algorithm computes the transformation matrix, further enhancing matching efficiency. Experimental results demonstrate that the proposed method exceeds the original AKAZE algorithm’s matching accuracy by 4∼15% on different image sets while achieving faster matching speeds. Under real-world conditions with UAV-captured aerial images of transmission towers, the proposed algorithm achieves over 95% matching accuracy, which is higher than other algorithms. Our proposed algorithm enables fast and accurate matching of transmission tower aerial images. Full article

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing values. This study focuses on two water-cooled turbo chillers installed in Tower A, Seoul, collecting a total of 118,464 data points over 3 years and 4 months. The dataset includes chilled water inlet and outlet temperatures ($T_1$ and $T_2$) and flow rate (${\dot{V}}_1$) and cooling water inlet and outlet temperatures ($T_3$ and $T_4$) and flow rate (${\dot{V}}_3$), as well as chiller power consumption (${\dot{W}}_c$). To evaluate the performance of various imputation techniques, we introduced missing values at a rate of 10–30% under the assumption of a missing-at-random (MAR) mechanism. Seven different imputation methods—mean, median, linear interpolation, multiple imputation, simple random imputation, k-nearest neighbors (KNN), and the dynamically clustered KNN (DC-KNN)—were applied, and their imputation performance was validated using MAPE and CVRMSE metrics. The DC-KNN method, developed in this study, improves upon conventional KNN imputation by integrating clustering and dynamic weighting mechanisms. The results indicate that DC-KNN achieved the highest predictive performance, with MAPE ranging from 9.74% to 10.30% and CVRMSE ranging from 12.19% to 13.43%. Finally, for the missing data recorded in July 2023, we applied the most effective DC-KNN method to generate imputed values that reflect the characteristics of the studied site, which employs an ice thermal energy storage system. Full article

With the continuous expansion of power grids, traditional manual inspection methods face numerous challenges, including low efficiency, high costs, and significant safety risks. As critical infrastructure in power transmission systems, power grid towers require intelligent recognition and monitoring to ensure the reliable and stable operation of power grids. However, existing methods struggle with accuracy and efficiency when processing large-scale point cloud data in complex environments. To address these challenges, this paper presents a comprehensive approach combining multi-sensor fusion and deep learning for power grid tower recognition. A data acquisition scheme that integrates LiDAR and a binocular depth camera, implementing the FAST-LIO algorithm, is proposed to achieve the spatiotemporal synchronization and fusion of sensor data. This integration enables the construction of a colored point cloud dataset with rich visual and geometric features. Based on the RandLA-Net framework, an efficient processing method for large-scale point cloud segmentation is developed and optimized explicitly for power grid tower scenarios. Experimental validation demonstrates that the proposed method achieves 90.8% precision in tower body recognition and maintains robust performance under various environmental conditions. The proposed approach successfully processes point cloud data containing over ten million points while effectively handling challenges such as uneven point distribution and environmental interference. These results validate the reliability of the proposed method in providing technical support for intelligent inspection and the management of power grid infrastructure. Full article