Transmission Tower Research Articles

Study on Windage Response Suppression of Large-Span Transmission Tower-Line System Using a Spring-Pendulum Dynamic Vibration Absorber

Insulators and conductors in transmission line systems are susceptible to wind-induced movements, especially when insulators are closely positioned to transmission towers, potentially leading to electrical discharge. To address this issue, this study proposes a spring-pendulum dynamic vibration absorber (SPDVA) for windage suppression in transmission line systems. Using a finite element model based on an actual transmission line project, the study investigates SPDVA’s effectiveness in mitigating windage responses under varying wind speeds. The analysis considers mass and stiffness parameters to elucidate SPDVA’s suppression mechanism. The results indicate that SPDVA outperforms the additional heavy hammer method in suppressing insulator windage responses and also affects the peak acceleration response at the insulator’s bottom. Increasing stiffness initially enhances SPDVA’s suppression effectiveness on both peak and mean square deviation windage angle responses, peaking at 10 kN/m stiffness, while increasing the mass of the mass block also enhances suppression effectiveness.

Seismic Vibration Control and Multi-Objective Optimization of Transmission Tower with Tuned Mass Damper Under Near-Fault Pulse-like Ground Motions

Although the wind load is usually adopted as the governing lateral load in the design of transmission towers, many tall transmission towers may be damaged or even collapse in high seismic intensity areas, especially under near-fault pulse-like ground motions. To study the seismic vibration control effect of a tuned mass damper (TMD) attached to transmission tower, parametric analyses are conducted in SAP2000 through CSI OAPI programming, including TMD parameters such as the mass ratio μ from 0.5% to 10%, the frequency ratio f from 0.7 to 1.2, and the damping ratio ξ from 0.01 to 0.2. Based on the obtained analysis results, artificial neural network (ANN) is trained to predict the vibration reduction ratios of peak responses and the corresponding vibration reduction cost. Finally, the NSGA-III algorithm is adopted to perform the multi-objective optimization of a transmission tower equipped with TMD. Results show that the vibration reduction ratios first increase and then decrease with the increase of frequency ratio, but first increase and then remain stable with the increase of mass ratio and damping ratio. In addition, ANN fitting can accurately predict the nonlinear relationship between TMD parameters and objective functions. Through multi-objective optimization with the NSGA-III algorithm, TMD can simultaneously and significantly reduce different peak responses of transmission towers under near-fault pulse-like ground motions in a cost-effective manner.

Reliability Analysis of Transmission Tower Based on Unscented Transformation Under Ice and Wind Loads

Due to the complexity of the transmission tower structure and the correlation between wind and ice loads in the actual project, it is difficult to analyze the reliability of transmission towers with traditional methods. To solve this problem, the unscented transformation (UT) principle is presented concisely and used in the reliability analysis of transmission towers in this paper. Moreover, the finite element model of the target transmission tower is created. The reliability indices of the transmission tower under various loading cases are evaluated using UT and analyzed relative to the outcomes of the Monte Carlo method (MCS). Lastly, by analyzing and validating a wine-cup shape tangent tower, the simulation results show that the UT yields reliability indices with less than 6% relative error compared with MCS results for the transmission towers with lower reliability, which are more important in engineering. Variations in error caused by the change in correlation coefficients among variables are small. Consequently, the efficiency of calculations is improved by the UT-based reliability calculations for transmission towers in the case of correlated variables, which better meet engineering application requirements. It is proved that the method of reliability analysis for transmission towers based on the UT is applicable.

Landslide Rapid Impact Assessment at Hydro Dam via THyCAS and UAS

The TNB Hydro Catchment Area Surveillance (THyCAS) is a web-based geographic information system (GIS) used to monitor hydropower dam catchment areas in Peninsular Malaysia. It can also be used for landslide rapid impact assessment by monitoring the surrounding landslide area. However, during the rainy season, satellite images are unable to provide the information required by users due to full cloud cover. Therefore, an Unmanned Aircraft System (UAS) is used to provide the data required by THyCAS by mapping the surrounding area affected by landslides and uploading the processed data in THyCAS. Thus, the user can get the required information regarding the landslide incident. Furthermore, with UAS Full Motion Video (FMV) data, the location of the collapsed power transmission towers can be acquired. In addition, any information on land changes that could jeopardise the surrounding dam area is gathered. Therefore, with the information from the rapid impact assessment provided by THyCAS and UAS data, a preliminary risk assessment plan can be established and implemented by the relevant parties. The results obtained from the rapid assessment can also be used as additional information to find out the actual cause of landslides in the area.

Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions

Overhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failure, regular inspections are essential to prevent power outages. To this end, this paper proposes a novel technique based on deep convolutional neural networks (CNNs) to classify high-voltage insulator surface conditions based on their image. Successful applications of CNNs in computer vision have led to several pretrained architectures for image classification. To use these pretrained models, a practitioner typically fine-tunes and selects one final model via a model selection stage and discards all other models. In contrast with many existing studies that use such a “winner-takes-all” approach, here, we identify the best subset of seven popular pretrained CNN architectures that are combined by soft voting to form an ensemble classifier. From a machine learning (ML) perspective, this focus is warranted because the convolutional base of each pretrained architecture operates as a feature extractor and an ensemble of them works as a combination of various feature extraction rules. Our numerical experiments demonstrate the advantage of the identified ensemble model to individual pretrained architectures.

Hidden Danger Detection and Identification System of Power Transmission Tower Based on YOLOV11

Hidden Danger Detection and Identification System of Power Transmission Tower Based on YOLOV11

Probabilistic Safety Assessment of Off-site Power System under Typhoon Considering Failure Correlation between Transmission Towers

High-intensity typhoons can cause failure to the structures, systems, and components of nuclear power plants (NPPs) and off-site power systems. Instances have occurred where failure of the off-site power system caused by typhoons has affected NPPs. The off-site power system has been in cases where a typhoon failed multiple transmission towers. In Korea, transmission towers have similar design criteria and material properties and experience similar wind characteristics, leading to a correlation in their failure probabilities. Assuming independent or totally dependent failure probabilities among the transmission towers is unrealistic. For a realistic PSA of the off-site power system due to typhoon-induced high winds, it is necessary to consider an appropriate failure correlation. This study performed a PSA of an off-site power system due to typhoon-induced high winds, considering the failure correlation between transmission towers. The failure correlation between transmission towers based on distance was calculated. The wind fragility of the off-site power system was convolved with the typhoon-induced high-wind hazard at the Kori NPP to calculate the probability of the NPP losing its power supply. Thus, applying the failure correlation of typhoon-induced high winds to the PSA of the off-site power system is considered realistic.

A proposed approach for failure analysis of lattice transmission towers considering geometric imperfection effects

A proposed approach for failure analysis of lattice transmission towers considering geometric imperfection effects

Protecting Electrical Workers in Conducting Locations with Restricted Movements

Conducting Locations with Restricted Movements (CLRs) pose unique electrical hazards due to the extensive presence of grounded conductive materials with which a person is likely to come into contact and the restricted freedom of movement of workers within these spaces. Extended physical contact is not solely a result of spatial constraints. It could also be associated with the specific tasks that workers need to execute. An example of this is work conducted on a transmission tower. This paper investigates the electrical safety measures necessary to protect operators in such environments. By examining the role of body resistance and the impact of different current pathways, this author highlights the inadequacy of the conventional disconnection of supply fault protection measure in CLRs. The paper discusses protective strategies, including supplementary equipotential bonding, use of double or reinforced insulation, electrical separation, and extra-low voltage systems. These measures are critical in mitigating the risk of electric shock and ensuring safety of workers in CLRs.

Buckling Behaviour of Q355 Angles with Simulated Local Damages at Bolted Connections

Transmission towers in service are highly susceptible to corrosion caused by environmental conditions. It is crucial to assess the residual load capacity of corroded angles in transmission towers. In this study, corrosion at the connection of angels was simulated by local damage using a mechanical cutting method, and compression tests and numerical simulations were performed to investigate the load capacity of corroded angles. A total of 24 angles were designed and tested in the experiments, and the parameters considered included the location and thickness of damage and slenderness. The local damage was designed on the loaded or non-loaded legs, with slendernesses of 80 and 140, and a thickness of damage of 1 mm and 2 mm. The residual load capacity, failure modes, and strain of the angles were analysed based on experimental results. Furthermore, corrosion was simulated by reducing the local thickness of angles using ABAQUS. The accuracy of numerical models was verified after comparing the numerical results with experimental data. Based on the verified model, parameter analysis was conducted, in which the slendernesses was extended to 100 and 120, and the local damage thickness was also set to be 0.5 mm and 1.5 mm to quantitatively study the influence on the residual load capacity. The tests results showed that with the damage depth at the ends of the angle increased, and the load capacity of the angle decreased by up to 6.7%. Finally, a design equation for calculating the residual load capacity of corroded angles was proposed using the numerical results. By comparing the design equation, experimental results, and load capacity calculated per existing standards, it was found that the load-bearing capacity of corroded angles can be accurately predicted by the equation.

Research on the Slip Model of Main Member Bar Connection of Transmission Tower Using Bolts

Research on the Slip Model of Main Member Bar Connection of Transmission Tower Using Bolts

Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model

A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model’s bearing capacity predictions and each input parameter, confirming the model’s broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application.

Computational analysis of wildland fire resistance for transmission line tower

Finite element (FE) analysis of the transmission towers under a fire event and the accompanying wind is presented. Several key aspects (including temperature-dependent non-linear material properties, geometric non-linearity, member eccentricity due to the single-leg bolted connection, and the temperature-dependent connection behaviour in both the axial and the rotational directions) are considered. A quasi-static analysis is also employed in the FE model using the explicit solver available in Abaqus. The member temperature variation during a realistic fire event is derived analytically based on the fire intensity and the resultant vertical gas temperature distribution so that the effect of the realistic wildland fire can be input as a member temperature profile. First, fire design guidance in terms of the most critical heating and wind pattern, as well as the effect of fire-affected heights are provided based on the case study results. Then, further fire analysis is carried out to investigate the most critical fire scenario and to evaluate the vulnerability of the tower during a realistic fire event. Lastly, an evaluation of a commonly used spray-on thermal insulation and its effectiveness in reducing the member temperature and preserving ultimate strength during a catastrophic wildland fire event are presented. The FE simulations revealed that a 45-degree wind associated with partial side heating leads to a more critical degradation of the overall strength, whereas the effect of fire height is less obvious. In terms of the fire scenario, a longer fire duration associated with a slower wind is more critical for the fire resistance/rating.

Risk assessment of transmission towers based on variable weight theory and matter-element extension model

Abstract The risk assessment of transmission towers is a critical approach to ensuring the safe and stable operation of power grids. In response to the increasingly frequent and severe impacts of seasonal permafrost on transmission towers in permafrost regions, this paper proposes a novel risk assessment method based on Variable Weight Theory and the Matter-Element Extension Model. By incorporating key influencing factors—such as the number of freeze-thaw cycles, soil moisture content, temperature fluctuations, soil dry density, melting settlement coefficient, and porosity ratio—an indicator system tailored to risk assessment in seasonal permafrost regions is established. Utilizing the Matter-Element Extension Model, this approach applies Variable Weight Theory to integrate both the best-worst method and the entropy weight method for calculating subjective and objective indicator weights. The risk level of the towers is then determined by their proximity to risk thresholds. Finally, a case study involving transmission towers in a specific region of a province in China is presented, where the assessment results are compared with the actual conditions to validate the rationality and effectiveness of the proposed method.

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Mathematical Model for Optimal Location of Cellular Transmission Towers

This paper applies a mathematical model for the optimal location of cellular signal transmission towers in a scenario different from the original scenario for which the model was developed. The proposed optimization aims to maximize the number of users served while maintaining a minimum signal quality standard. To validate the model, measurement campaigns were carried out at 44 points in a residential setting in the municipality of Castanhal-PA, at frequencies of 1800 MHz and 2600 MHz. The model outputs provide the received power, the average loss and the optimal location of the new tower to be installed.

A cloud 15kV-HDPE insulator leakage current classification based improved particle swarm optimization and LSTM-CNN deep learning approach

Real-time insulator leakage current classification is crucial in preventing the pollution flashover phenomenon and providing appropriate maintenance schedules in high-voltage transmission towers. However, current methodologies only utilize traditional artificial neural networks, which have limitations when performing big data analysis. This research developed a novel cloud 15kV-HDPE insulator leakage current classified framework, utilizing a long short-term memory convolutional neural network (LSTM-CNN). The hybrid model structure is optimized through hyperparameter fine-tuning based on improved particle swarm optimization (IPSO), which reduces human effort and considerable time compared with PSO and random search (RS) techniques. The IPSO-LSTM-CNN model can productively identify correlations between selected weather features and target leakage current levels of 15kV-HDPE insulators. LSTM efficiently captures long-term patterns in sequential data, while CNN layers competently extract high-level dependency in time-invariant information. Four 15kV-HDPE insulators’ datasets, collected in high-voltage transmission lines in the coastal area of Taiwan for more than one year, are deployed for analyzing and comparing classified performance. Other conventional models are developed to evaluate and compare classified performance with the proposed IPSO-LSTM-CNN approach, which acquires the most significant enhancement of 48.08 % loss, 45.91 % validating loss, 52.57 % MAE, 35.47 % validating MAE, 47.34 % MSE, 27.02 % validating MSE, 9.15 % PRE, 3.40 % validating PRE, 4.76 % REC, and 6.17 % validating REC. The experiment outcomes demonstrate that the developed IPSO-LSTM-CNN model acquires improved robustness and accuracy in the leakage current classified capability of 15kV-HDPE insulators.

Stability behavior of T-shaped retrofitting cross bracings in existing transmission towers

To analyze the stability behavior of T-shaped retrofitting cross bracings, finite element models were developed and validated by full-scale experiments of cross bracings available in the literature. Effects of stress ratio, nominal slenderness ratio, width-to-thickness ratio, and yield strength on the failure mode and bearing capacity were considered for T-shaped retrofitting cross bracings without auxiliary members (TCB) and with auxiliary members (TCBA). Results show that the failure modes of T-shaped retrofitting cross bracings are out-of-plane bending of the entire brace. As the stress ratio decreases, the failure modes transition to the bending of the longer section of the brace. When the nominal slenderness ratio is below 160 for TCB, local buckling dominates and nominal slenderness ratio has few effects on the bearing capacity. Also, stress ratio has a minimal impact on bearing capacity when it is less than −0.2. In addition to above, bearing capacity is related to nominal slenderness ratio and stress ratio. A higher width-to-thickness ratio increases the nominal stability coefficient for TCB, but has a negligible effect on TCBA. Increasing yield strength can effectively enhance the bearing capacity of TCB when stress ratio is below 0, while yield strength has little impact in other conditions.

Numerička analiza titrajnog odziva dalekovoda velikog raspona prijenosnih vodova izazvanog vjetrom

Based on the engineering background related to ±800 kV ultra-high voltage (UHV) transmission lines spanning the Yellow River, finite element models were developed for single-tower and transmission tower-line systems, with their wind-induced responses under wind loads were simulated and analysed. In addition, the coupling effect in a large-span transmission tower line system was investigated. The results showed that the natural vibration frequency of the tower-line system was slightly lower than that of a single tower and that the torsional natural frequency was the most significantly reduced. The transmission tower-line system has a maximum displacement of 1.92 times that of a single tower when the wind angle is 0°. The maximum axial force and stress in the tower-line system are 1.6 times and 1.37 times higher than those in a single tower. The interaction between the tower and wires significantly affects the wind-induced response of the tower body. Hence, considering the dynamic coupling effect of the tower-line transmission system is important when designing a tower.

Failure intervention of transmission line tower primary legs using bi-angled cruciform retrofitting

Retrofitting has been suggested by researchers as an intervention measure against failure of transmission towers which needs to be done with minimal disruption. Retrofitting of the primary leg members of transmission line towers by bi-angled cruciform arrangement has been shown in literature to be effective. However, the influence of stresses and deformations existing in the leg member prior to retrofitting on the capacity of the final retrofitted member is not known. Therefore, this study assesses the effectiveness of bi-angled cruciform sections under preload conditions through experiment followed by a numerical study. The level of preload in the primary leg member with respect to its theoretical capacity, slenderness ratio and width-thickness ratio of the angle section were the parameters. The results suggest that bi-angled cruciform retrofit is more effective at lower slenderness and width-thickness ratios and the reduction in capacity of retrofitted member is more noticeable till 50 % preload after which the changes are not significant. While the former two parameters lead to lower load share in the retrofitting member, higher preloads however lead to more equal load share. The application of this retrofitting pattern is demonstrated numerically with a full-scale tower model simulated under code specified load cases. Critical load cases leading to tower failure before design loads were identified and the retrofitting was incorporated into the model for these load cases. The analysis showed that the final loads attained in these load cases improved by upto 70 % thus confirming the potential of this retrofitting technique in preventing tower failure.