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The icing on the towers in transmission lines is mainly affected by meteorological factors such as temperature and humidity. This paper presents an evaluation model for the icing state of transmission towers based on meteorological factors and the random forest (RF) algorithm. Firstly, based on the grid meteorological data and the coordinates of the towers in the transmission lines within Jiangxi Province, China, the meteorological factors corresponding to each tower were matched, and an ice coverage criterion model based on physical conditions was established. The results obtained based on physical criteria were compared with the actual icing monitoring data of transmission towers to verify the validity of the proposed criteria. An RF classifier is adopted to construct a dependency mapping model between meteorological factors and the icing state of towers. Only a small amount of data from seven transmission lines was used to train the model, successfully predicting the icing state of 32,496 data sets with an accuracy and recall of 89.4% and 87.01%, respectively. Its performance was far superior to that of back propagation neural network (BPNN), support vector classifier (SVC) and least squares support vector machine (LSSVM) models, verifying the feasibility of the proposed method in practical applications.

This manuscript reviews previous literature on filament-wound polymer composites and their potential applications as cross arm structures in latticed transmission towers. The current trends of cross arms implement pultruded glass fibre-reinforced polymer composites without any additional configurations. However, extreme tropical climate and dynamic wind loads can cause a high risk of sudden failure due to creep, followed by laminate crack propagation, which can induce structural failure. Glass fibre-reinforced polymer composites are more resilient in corrosion resistance, strength, extreme conditions, and life serviceability according to previous literature. The composite can also function as a good insulator in lightning impulse strength of composite cross arms. It is suggested that the current cross arm design has to adopt core structure as reinforcement to the structure by using filament winding process for long-term structures. Hence, the composite structure can withstand extreme environmental conditions via the filament winding process. Thus, this manuscript is expected to deliver a state-of-art review on the manufacturing process, perspectives, and potential of filament-wounded composite as cross arms in transmission towers.

Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a spatial hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance.

Under extreme terrain conditions, there is no site to arrange the control rope retraction system. In this paper, a flexible guide rail hoisting system is designed to replace the traditional transmission tower wind rope hoisting method. Taking the JC27151CG tower assembly as the object, the design of the flexible guide rail hoisting system is carried out, and the ADAMS software is used to establish the dynamic model of the flexible guide rail hoisting system. The mechanical analysis of the flexible guide rail hoisting system is carried out from the perspectives of statics and dynamics.

Landslides induced by heavy rainfall are common in southern China and pose significant risks to the safe operation of transmission lines. To ensure the reliability of transmission line operations, this paper presents a stability prediction model for transmission tower slopes based on the Improved Sand Cat Swarm Optimization (ISCSO) algorithm and Support Vector Machine (SVM). The ISCSO algorithm is enhanced with dynamic reverse learning and triangular wandering strategies, which are then used to optimize the kernel and penalty parameters of the SVM, resulting in the ISCSO-SVM prediction model. In this study, a typical transmission tower slope in southern China is used as a case study, with the transmission tower slope database generated through orthogonal experimental design and Geo-studio simulations. In addition to traditional input features, an additional input—transmission tower catchment area—is incorporated, and the stable state of the transmission tower slope is set as the predicted output. The results demonstrate that the ISCSO-SVM model achieves the highest prediction accuracy, with the smallest errors across all metrics. Specifically, compared to the standard SVM, the MAPE, MAE, and RMSE values are reduced by 70.96%, 71.41%, and 57.37%, respectively. The ISCSO-SVM model effectively predicts the stability of transmission tower slopes, thereby ensuring the safe operation of transmission lines.

Currently, pultruded glass fibre-reinforced polymer (pGFRP) composites have been extensively applied as cross-arm structures in latticed transmission towers. These materials were chosen for their high strength-to-weight ratio and lightweight characteristics. Nevertheless, several researchers have discovered that several existing composite cross arms can decline in performance, which leads to composite failure due to creep, torsional movement, buckling, moisture, significant temperature change, and other environmental factors. This leads to the composite structure experiencing a reduced service life. To resolve this problem, several researchers have proposed to implement composite cross arms with sleeve installation, an addition of bracing systems, and the inclusion of pGFRP composite beams with the core structure in order to have a sustainable composite structure. The aforementioned improvements in these composite structures provide superior performance under mechanical duress by having better stiffness, superiority in flexural behaviour, enhanced energy absorption, and improved load-carrying capacity. Even though there is a deficiency in the previous literature on this matter, several established works on the enhancement of composite cross-arm structures and beams have been applied. Thus, this review articles delivers on a state-of-the-art review on the design improvement and mechanical properties of composite cross-arm structures in experimental and computational simulation approaches.

The adoption of pultruded glass fibre-reinforced polymer (pGFRP) composites as a substitute for traditional wooden cross-arms in high transmission towers represents a relatively novel approach. These materials were selected for their high strength-to-weight ratio and lightweight properties. Despite various studies focusing on structures improvement, there still have a significant gap in understanding the deformation characteristics of full-scale cross-arms under actual operational loads. Existing study often concentrate on small coupon scale and laboratory condition, leaving a gap in understanding how the cross-arm behavior in full-scale acting on actual weather condition. This study aims to investigate the load-deflection and long-term creep behavior of a pGFRP cross-arm installed in a 132 kV transmission tower. The pGFRP cross-arm was load-tested on a customized rig in an open environment. Using the cantilever beam concept, deflection was analyzed and compared to wood cross-arms. Finite element analysis validated results, and long-term deformation under high-stress loads was assessed. The pGFRP cross-arms showed lower deflection at working loads compared to Balau wood, due to the latter’s higher elastic modulus and flexibility specifically at Point Y3, the critical issues necessitated reinforcement strategies. pGFRP cross-arms withstood higher bending stress, showing 32% less deflection under normal conditions and 15% less under broken wire conditions than Balau wood. Additionally, the creep strength of wood was 34% lower than that of pGFRP cross-arms. Besides that, the pGFRP cross-arm have highest elastic modulus than Balau-wood, shows that the composite cross-arm have better structural strength, resisting deformation and higher flexibility materials. Finite element analysis (FEA) confirmed these results with the relative error between them less than 1%. Consequently, the investigation into pGFRP cross-arm deformation behavior in this paper serves as a foundational framework for future research endeavors specifically for high transmission tower and other structural application.

Transmission towers are easily affected by various meteorological and geological disasters. In this paper, a transmission tower tilt state assessment approach—based on high precision and dense point cloud from UAV LiDAR—was proposed. First, the transmission tower point cloud was rapidly located and extracted from the 3D point cloud obtained by UAV-LiDAR line patrol. A robust histogram local extremum extraction method with additional constraints was proposed to achieve adaptive segmentation of the tower head and tower body point cloud. Second, an accurate and efficient extraction and simplification strategy of the contour of the feature plane point cloud was proposed. The central axis of the tower was constrained by the contour of the feature plane through the four-prism structure to calculate the tilt angle of the tower and evaluate the state of the tower. Finally, the point cloud of tower head from UAV-based LiDAR was accurately matched with the designed tower head model from database, and a tower head state evaluation model based on matching offset parameters was proposed to evaluate tower head tilt state. The experimental results of simulation and measured data showed that the calculation accuracy of the tilt parameters of transmission tower body was better than 0.5 degrees, that the proposed method can effectively evaluate the risk of tower head with complex structure, and improve the rapid and mass intelligent perception level of the risk state of the transmission line tower, which has a wide prospects for application.

Fibre-reinforced polymer (FRP) composites have been selected as an alternative to conventional wooden timber cross arms. The advantages of FRP composites include a high strength-to-weight ratio, lightweight, ease of production, as well as optimal mechanical performance. Since a non-conductive cross arm structure is exposed to constant loading for a very long time, creep is one of the main factors that cause structural failure. In this state, the structure experiences creep deformation, which can result in serviceability problems, stress redistribution, pre-stress loss, and the failure of structural elements. These issues can be resolved by assessing the creep trends and properties of the structure, which can forecast its serviceability and long-term mechanical performance. Hence, the principles, approaches, and characteristics of creep are used to comprehend and analyse the behaviour of wood and composite cantilever structures under long-term loads. The development of appropriate creep methods and approaches to non-conductive cross arm construction is given particular attention in this literature review, including suitable mitigation strategies such as sleeve installation, the addition of bracing systems, and the inclusion of cross arm beams in the core structure. Thus, this article delivers a state-of-the-art review of creep properties, as well as an analysis of non-conductive cross arm structures using experimental approaches. Additionally, this review highlights future developments and progress in cross arm studies.

Displacement prediction of transmission towers is essential for the early warning of transmission network deformation. However, there is still a lack of prediction on the ground subsidence of the tower foundation. In this study, we first used the multi-temporal interferometric synthetic aperture radar (MT-InSAR) approach to acquire time series deformation for the transmission lines in the Salt Lake area. Based on the K-shape clustering method and field investigation results, towers #95 and #151 with representative foundation deformation characteristics were selected for displacement prediction. Combined with field investigations and the characteristics of saline soil in the Salt Lake area, the trigger factors of transmission tower deformation were analyzed. Then, the displacement and trigger factors of the transmission tower were decomposed by variational mode decomposition (VMD), which could closely connect the characteristics of the foundation saline soil with the influence of the trigger factors. To analyze the contribution of each trigger factor, the maximum information coefficient (MIC) was quantified, and the best choice was made. Finally, the hyperparameters of the long short-term memory (LSTM) neural networks were optimized using a convolutional neural network (CNN) and the grey wolf optimizer (GWO). The findings reveal that the refined deep learning models outperform the initial model in generalization potential and prediction precision, with the CNN–LSTM model demonstrating the highest accuracy in predicting the total displacement of tower #151 (RMSE and R2 for the validation set are 0.485 and 0.972, respectively). Given the scant research on the multifactorial influence on the ground subsidence displacement of transmission towers, this study’s methodology offers a novel perspective for monitoring and early warning of ground subsidence disasters in transmission networks.