Operation Of Transmission Lines Research Articles

Field Natural Experimental Study of Ice Growth Characteristics of Split Conductor and Single Conductor on Hybrid Freezing Icing

Abstract Transmission line icing seriously endangers the safe operation of transmission lines. At present, most of the research on transmission line icing is based on numerical simulation, and the growth law of transmission line ice in a natural environment has not been explored and studied. In this paper, natural ice-covering experiments are carried out in the Xuefeng Mountain Field (Natural) Ice Covering Test Base of Chongqing University, and single conductor, three-split, and four-split conductor ice-covering change rules in the mixed rime ice environment are explored. Test results found that the wind direction has a greater impact on the ice, and the windward side of the conductor ice is much larger than the leeward side. The downwind side of the ice of both single conductor and split conductor is smaller than the upwind side. The weight of ice on the split conductor line on the downwind side is about 86% of that on the upwind side. The results can provide a reference for the design of external insulation of UHV DC transmission lines.

Enhancing transmission type frame structures: A BBO algorithm-based integrated design approach.

The stable and site-specific operation of transmission lines is a crucial safeguard for grid functionality. This study introduces a comprehensive optimization design method for transmission line crossing frame structures based on the Biogeography-Based Optimization (BBO) algorithm, which integrates size, shape, and topology optimization. By utilizing the BBO algorithm to optimize the truss structure's design variables, the method ensures the structure's economic and practical viability while enhancing its performance. The optimization process is validated through finite element analysis, confirming the optimized structure's compliance with strength, stiffness, and stability requirements. The results demonstrate that the integrated design of size, shape, and topology optimization, as opposed to individual optimizations of size or shape and topology, yields the lightest structure mass and a maximum stress of 151.4 MPa under construction conditions. These findings also satisfy the criteria for strength, stiffness, and stability, verifying the method's feasibility, effectiveness, and practicality. This approach surpasses traditional optimization methods, offering a more effective solution for complex structural optimization challenges, thereby enhancing the sustainable utilization of structures.

A bird droppings flashover analysis method based on the combination of electric field distortion and spatial electric field distribution

To study the flashover of air gap caused by bird droppings shorting air gap and the influence of space electric field change on it, the distortion electric field simulation, space electric field simulation, and related flashover test of transmission line bird droppings falling process were carried out. By analyzing the specific influencing factors of bird droppings flashover fault, it is pointed out that the electric field distortion caused by bird droppings will affect the spatial electric field distribution around the insulator and thus affect the air gap flashover. This study analyzes the specific causes of bird dropping flashover of transmission line insulators, reduces the number of bird damage trips of transmission lines, reduces the cost of operation and maintenance of transmission lines, improves the intrinsic safety of transmission lines, and ensures the safe and reliable operation of power grids.

Defect identification of electricity transmission line insulators based on the improved lightweight network model with computer vision assistance

This work aims to ensure the safe operation of electricity transmission lines and reduce costs and maintenance difficulties. It studies the application of computer vision (CV) in the defect identification of electricity transmission lines. In addition, this work proposes a method to improve the lightweight network model to provide an effective identification model to solve the problem of electricity transmission line defects. Firstly, GraphCut segmentation and Laplace algorithms are employed to expand and sharpen the electricity transmission line image. Secondly, in light of the Depth Separable Convolution algorithm, a defect detection model for the electricity transmission line insulator is proposed based on the You Only Look Once 4 (YOLOv4) network. Moreover, MobileNetV1 is utilized to improve this lightweight network model. Finally, this work uses ImageNet, a large public dataset, to validate the proposed model experimentally. The research results reveal that: (1) In the model testing results, all research indicators of the model are greater than 90 %, indicating an excellent detection accuracy of this model. (2) The improved YOLOv4 model can increase the detection speed to 53 frames/s at the cost of 2.4 % accuracy. (3) After image sharpening, the improved YOLOv4 model has promoted the insulator defects' detection ability to a certain extent. The above outcomes suggest that the improved YOLOv4 model can predict more efficiently and accurately and reduce unnecessary false positives. This illustrates that the proposed model is feasible and is expected to be applied to the defect identification of electricity transmission lines in practice. These findings fully demonstrate this work's vital value in enhancing the prediction efficiency and accuracy, thus offering a strong preference for the defect identification of electricity transmission lines in practical applications.

Design and Fabrication of Spoof Surface Plasmon Transmission Line Operating at High Frequency

Design and Fabrication of Spoof Surface Plasmon Transmission Line Operating at High Frequency

Study on Real-Time Spatial Distance Acquisition Method of Transmission Line Based on Laser Point Cloud and Image Monitoring

This paper introduces the principle of safety distance diagnosis of transmission lines by using airborne LiDAR detection technology. This project intends to realize the effective segmentation of ground objects and non-ground objects through the point cloud automatic filtering technology considering the surface fluctuation characteristics. Then, this paper realizes the initial recognition of ground objects based on spatial dimension information. Then this paper extracts the ground object information based on the image data with high precision. The 3D nodes of transmission lines are obtained by Hough transform and block centroid algorithm, and the floor features are extracted by area growth algorithm. The plant feature is extracted by using plant vertex and boundary point. The distance between the power line and the surrounding objects can be measured, and the elevation of the wire and the surrounding environment can be judged accordingly. The algorithm is tested by using the observation data of unmanned airborne LiDAR, and the simulation results show that the proposed method is feasible. Through the identification of buildings, plants and other objects along the line, the harm degree of them on the power line is judged and the effective treatment is carried out. This ensures the safe operation of transmission lines.

Fragility Analysis of a Transmission Tower-Line System Subjected to Wind and Ice Loads Considering Fatigue Damage

Disasters such as ice and wind can pose a serious threat to the normal operation of power transmission lines. Wind-induced fatigue damage can further reduce the load-carrying capacity of transmission towers, increasing their fragility to ice and wind disasters. To assess the comprehensive capability of transmission towers to resist extreme ice and wind disasters, this paper proposes a failure probability evaluation framework for transmission towers considering wind-induced fatigue damage under the coupled effect of ice and wind. Taking a certain transmission line in Hunan as an example, the failure probability caused by ice and wind disasters is calculated for different years of service. First, based on the historical meteorological data in three cities in Hunan, a joint probability model of wind speed and wind direction considering their correlation is established using the copula function. Then, based on this probability model, the wind-induced fatigue damage of transmission towers is calculated using the Miner linear damage theory and the S-N curve. Subsequently, the fragility of the tower under the coupled load of ice and wind is calculated for different years of service. Finally, by combining the structural fragility function with the joint probability distribution model of ice thickness and wind speed, the collapse probability of the transmission tower under the action of ice and wind disasters is calculated. The results indicate that the influence of wind-induced fatigue damage cannot be ignored when transmission towers encounter ice and wind disasters. With increasing service time, the ability of transmission towers to resist ice and wind disasters gradually decreases, and the failure probability also increases under extreme ice and wind conditions.

Study on the influence of orthogonal double pendulum on the ice shedding of transmission lines

ABSTRACT Ice shedding and vibration of ice-covered transmission lines can pose a serious threat to the safe operation of power lines in heavy ice areas. However, the common preventative practice of installing anti-vibration hammers, spacers and other devices on the line has limited effects on the safe operation of transmission lines. This paper focused on the simulation and experimental study of the influence of orthogonal double pendulum damper, a new type of anti-vibration device for transmission lines, on the ice shedding and vibration reduction of transmission lines. The 3-DOF calculation model of the orthogonal double pendulum damper was established, and the influence of the parameters of orthogonal double pendulum damper on the ice shedding of the line was simulated and analysed. In addition, natural ice shedding tests of lines were carried out at Xuefeng Mountain National Field Station, and ice shedding and vibration displacement response of lines was measured after the installation of orthogonal double pendulum dampers. The results showed that The three main structural parameters of mass, pendulum length and arm length can affect the maximum amplitude of ice shedding and vibration of transmission lines. The mass of the hammerhead has an effect on the vibration in both vertical and horizontal directions; the pendulum length mainly affects the vibration of the line in the horizontal direction; the arm length mainly affects the vibration in the vertical direction.

Comparative assessment of fault detection methods in transmission line during power swing: analyzing single and hybrid networks for the southern Kerala grid

The blocking function of power oscillation is a highly effective feature found in distance relays. Its purpose is to prevent unintentional tripping of transmission lines. It is crucial to accurately differentiate between faults and power oscillations to minimise the occurrence of widespread power outages and monetary losses. Therefore, the detection and classification of faults arising from power swings are critical challenges in ensuring the smooth operation and overall health of transmission lines, particularly in extra-high-voltage (EHV) and ultra-high-voltage (UHV) power systems. Modern methods rely on machine learning methods which are in their infancy. The paper proposes to identify various fault conditions in transmission lines during power swing by using a hybrid Neural Network architecture. In this research, different designs such as 2-D convolution neural network (2D-CNN), 1-D convolution neural network (1D-CNN), long short-term memory (LSTM), and CNN-LSTM hybrid networks are investigated to understand the effect of various deep learning approaches. Along with the southern Kerala grid, the standard 9-bus system is selected as a test case to examine various fault circumstances of the system during power swing. Multiple tests were conducted to determine the optimal deep learning architecture, including model parameters and configurations, for accurate fault detection. For the 9 bus system, the 1D CNN network performs better with an accuracy of 98.70%, and for the Kerala grid, both networks are competitive, but the CNN-LSTM Hybrid method slightly outperforms the 1D CNN with an accuracy of 92.40%.

Analysis of Solar Radiation Differences for High-Voltage Transmission Lines on Micro-Terrain Areas

The stable operation of high-voltage transmission lines is significantly affected by atmospheric icing. Research on the physical processes of icing and de-icing of transmission lines in micro-terrain, as well as the factors affecting them, is a crucial theoretical foundation for enhancing current icing prediction capabilities and guiding the planning of transmission lines in mountainous areas. The difficulty lies in the fact that, unlike the calculation of surface radiation, the amount of radiation received by the lines is affected by a combination of terrain, environmental shading, and the orientation of the lines. Therefore, this work initially establishes a method for calculating the total amount of radiant heat received per unit length of the line throughout the day at various heights from the ground, based on the angle of solar incidence and the three-dimensional spatial position of the lines. Furthermore, a method of mapping the regional heat radiation by gridding the direction of the lines was proposed, providing the daily heat radiation and equivalent Joule heat. The proposed mapping method supports anti-icing planning for high-voltage transmission lines in micro-terrain areas.

Transmission line defect detection based on feature fusion

Transmission lines are the lifeblood of the power system, and regular line inspections are required to ensure the operation of transmission lines. However, due to the special high-voltage environment of transmission lines, the pictures collected by UAVs are characterized by many background interferences and small defect samples, and it is difficult to directly detect line inspection methods based on deep learning. For this reason, this paper proposes a high and low-dimensional fusion framework, which divides the target detection task into four modules, and greatly reduces the technical volume of the model while retaining the high-dimensional features through the collaboration between modules. This paper also introduces the window attention mechanism based on YOLOv7, which allows the model to focus on local information and improve the model’s detection ability on small targets and also introduces the Wise-IOU loss to improve the model’s prediction accuracy and inference time. Experiments prove that the method in this paper can significantly improve the accuracy of model prediction while meeting the speed requirements of industrial scenarios.

A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance.

Simulation analysis on the synergistic effect of vegetation ashes and charged particles on the gap electric field distortion

Hill fires, a sort of ultra-natural disaster that poses a severe threat to the safe and stable operation of transmission lines, have become more frequent in recent years. Currently, the modeling research on the transmission line gap electric field distortion under hill fire conditions does not consider the synergistic effect of charged particles and ash particles, which would lead to the imperfect gap electric field distortion mechanism. Herein, a coupled multi-physics field simulation model of electric, thermal, fluid, chemical field and particle motion was constructed to analyze the electric field distribution and particle motion. Compared to the related simulation models, this study improves the simulation accuracy by around 407% by optimizing the structure and parameters of the simulation model. The FEM software COMSOL Multiphysics simulation results show that the percentage of ash entering the examined region of the AC conductor was 34.1% higher, and the percentage of connected ash was 45% higher, increasing the likelihood of gap breakdown compared to the DC conductor; the charge of ash (10−14 to 10−11) is significantly less than the saturation charge of ash (⩾10−3). Therefore, the charged particles change the motion characteristics of the ash primarily through the electric field force and dielectrophoresis force, while the ash-induced distortion of the electric field affects the spatial distribution of the charged particles, eventually, the background electric field undergoes significant distortion by the synergistic effect of the two. The results examine the inherent mechanism of gap electric field distortion at the microscopic level, which can provide theoretical support for understanding the transition phase of transmission line gaps from insulation to break down under hill fire conditions.

Weather-Domain Transfer-Based Attention YOLO for Multi-Domain Insulator Defect Detection and Classification in UAV Images.

Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in complex weather scenarios, with small and inconsistent shapes. These insulators under various weather conditions could result in low-quality images captured, limited data numbers, and imbalanced sample problems. Traditional detection methods often struggle to accurately identify defect information, resulting in missed or false detections in real-world scenarios. In this paper, we propose a weather domain synthesis network for extracting cross-modality discriminative information on multi-domain insulator defect detection and classification tasks. Firstly, we design a novel weather domain synthesis (WDSt) module to convert various weather-conditioned insulator images to the uniform weather domain to decrease the existing domain gap. To further improve the detection performance, we leverage the attention mechanism to construct the Cross-modality Information Attention YOLO (CIA-YOLO) model to improve the detection capability for insulator defects. Here, we fuse both shallow and deep feature maps by adding the extra object detection layer, increasing the accuracy for detecting small targets. The experimental results prove the proposed Cross-modality Information Attention YOLO with the weather domain synthesis algorithm can achieve superior performance in multi-domain insulator datasets (MD-Insulator). Moreover, the proposed algorithm also gives a new perspective for decreasing the multi-domain insulator modality gap with weather-domain transfer, which can inspire more researchers to focus on the field.

RCDAM-Net: A Foreign Object Detection Algorithm for Transmission Tower Lines Based on RevCol Network

As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird’s nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottleNeck structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves mAP50, Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms.

Prediction method for response characteristics parameters of isolated-span overhead lines after ice-shedding based on finite element simulation and machine learning

In response to the problem of insufficient research on the prediction for response characteristics parameters of isolated-span transmission lines under various ice-shedding modes, study on the prediction for ice-shedding response parameters of isolated-span overhead lines is conducted in this paper based on finite element simulation and machine learning. Firstly, the finite element simulation model of isolated-span overhead line is established and verified. Seven model input parameters are determined by considering 12 typical ice-shedding modes and ice-shedding labels are introduced. Six key output parameters reflecting the electrical and mechanical characteristics of the ice-shedding transmission line are obtained by feature extraction. Secondly, the output parameters of the ice-shedding transmission line under different input parameter conditions are obtained by using the finite element simulation method. A total of 1400 sets sample data are constructed. Then, four typical regression prediction models of BP (Back Propagation Algorithm), RBF (Radial Basis Function Algorithm), SVM (Support Vector Machine Algorithm) and RF (Random Forest Algorithm) are established to predict the key output parameters of ice-shedding transmission lines. Finally, the optimal prediction model for ice-shedding response characteristics parameters of isolated-span transmission lines is selected and the application effect is analyzed by actual ice-shedding case. It is found that the RF prediction model is the optimal prediction model among the four regression prediction models. The prediction method for ice-shedding response characteristics parameters of isolated-span transmission line proposed in this paper can accurately predict the response characteristics parameters of ice-shedding transmission lines which can provide some references for the safe operation of isolated-span transmission lines in heavy ice areas.

Analysis and experimental study on the cause of fracture of a Transmission line’s Drainage connecting plate

The transmission line is exposed to the outdoors for a long time, and the operation environment is complex, which makes the line equipment prone to corrosion, wore and heat, etc. The fracture of the drainage plate of the tensioning wire is one of the problems that often occur in the operation of the transmission line. Based on the systematic analysis of the problem of the fracture of the drainage plate of a transmission line, the causes of the fracture of the drainage plate are obtained, and the corresponding rectification measures are proposed, which has an important reference for the design, construction, operation and maintenance of the subsequent transmission line.

Analysis and Suggestions for the Self-blast Problem of Glass Insulators on Transmission Lines

By virtue of many advantages such as good self-cleaning ability, outstanding mechanical performance, and excellent anti-vibration-fatigue ability, the glass insulators have been widely used in various alternating-current and direct-current transmission lines. However, the self-blast incidents of glass insulators have frequently occurred in transmission lines with various voltage levels, seriously affecting the safe operation of transmission lines. Herein, the main causes of self-blast problem of glass insulators are summarized and analysed from different perspectives, and some corresponding suggestions have been proposed to reduce the self-blast of glass insulators. In addition, the relevant prospects are provided for the future research work about the glass insulators. The viewpoints in this article have important theoretical and practical significance for improving the safety and reliability of transmission lines and ensuring the stable operation of electrical power systems.

Research on up and down line control method of 500kV live working robot

It is a developing trend for transmission line operation robots to replace manual operations in line operation and maintenance. However, the existing on-line methods of robots are complex and inefficient, which seriously affects the progress of the entire operation and maintenance. In order to solve this problem, this paper studies the automatic on-line and off-line control method of live working robot for 500kV transmission lines. Firstly, the on-line and off-line structure of the robot and its operating mechanism are analyzed, and on this basis, the relevant mathematical model is established. Through the analysis of the model, cascade fuzzy PID is used to control the robot’s attitude leveling. Finally, through MATLAB software simulation, the cascade fuzzy PID control effect is the best.

RESEARCH ON POWER TRANSMISSION CHANNEL CHANGE DETECTION BASED ON MULTI-TEMPORAL POINT CLOUD DATA

Abstract. Airborne LiDAR can directly obtain 3D information of ground objects. By comparing the multi-temporal LiDAR data, ground objects change information of power transmission channel can be detected, providing data support for transmission line operation and maintenance. In this paper, an improved ICP algorithm based on multi-temporal LiDAR point cloud data power transmission channel ground object change detection method is proposed. Firstly, based on the classification of point cloud data, a two-level matching method of multi-temporal point cloud data considering the characteristics of power transmission channel was proposed to achieve accurate registration of point cloud data. Then, change detection and analysis of different types of ground feature point cloud data were carried out through elevation difference. Finally, cluster analysis was carried out on the changed ground feature points to generate multi-temporal relative ratio analysis report. Experimental results show that the proposed method can effectively detect power transmission channel changes.