Transmission Line Sag Measurement Algorithm Based on Binocular Vision 3D Reconstruction

With the planning and implementation of national smart grid construction, on-line monitoring technology, as one of the key technologies of smart transmission, is also developing towards intelligence. With the construction of smart grid, the number of UHV long-distance and large-capacity transmission lines is increasing, and the load density of the grid is also increasing. As an important component of power system operation, the safety and reliability of power lines are very important. At the same time, with the popularization of helicopter inspection of power transmission lines, it not only provides opportunities but also challenges for automatic detection of defects in power transmission lines by image processing and recognition technology. How to effectively obtain basic information of power line operation from power line images obtained by helicopter inspection has become a research hotspot. Using automatic extraction method of image recognition can not only improve work efficiency and detection accuracy, reduce patrol personnel, but also overcome various defects of manual methods, and finally improve the safety of power line patrol and the accuracy of detection results.

This paper proposes a climbing robot for inspection of high-voltage power transmission lines. The robot is based on a self-balance dual-arm mechanism. It has the capability of avoiding dampers, spacers, suspension clamps and strain clamps, spanning between lines and climbing on much steep cable. It is dedicated for the inspection of single cable, double bundle cables and four bundle cables whether the cables are powered on or not. This paper mainly demonstrates the mechanical design, the principle of robot altitude control and the driving technique of the joints. The simulation results based on ADAMS confirm that the mechanical design is reasonable. The practical model of the robot which could be manipulated by a remote controller is also mentioned in this paper.

Abstract: The paper presents sharing several of experiences and practices on smart robotic application for overhead transmission line maintenance and inspection. The robotic puller passes over barriers below the power line, such as the road with traffic, power distribution lines, river, or vegetation making tasks achieved conveniently, safely, and rapidly without impact on nearby communities.The target of the research is to create a mobile robot prototype for inspection of overhead power lines. The inspection robot shall crawl along the ground wire and transpose autonomously across installed equipment on the ground wire, such as vibration dampers, suspension clamps, compression dead ends, etc. In addition, the inspection robot is able to take photos and videos during a transmission line inspection . Using the robot, transmission line inspection’s labor cost can be reduced, and the new method helps improve patrol and inspection efficiency, comparing to the conventional manpower method. Trough utilization of the new maintenance and inspection robots, utilities can minimiz transmission line operation & maintenance budget.

The inspection of power transmission lines using unmanned aerial vehicles primarily relies on object detection. However, the continuous emergence of new obstacle types necessitates frequent updates to detection models, leading to substantial retraining costs. To address this challenge, we propose a novel framework named IViT, which integrates incremental learning with a hybrid CNN-Transformer architecture for improved identification. We combined knowledge distillation with the elastic response selection distillation strategy to enhance detection performance for old classes and strengthen knowledge retention through star convolutional residual blocks constructed via element-wise multiplication. We designed a separable convolution aggregation block that integrates PConv with an attention mechanism, effectively merging global and local information to improve detection accuracy. Finally, we unified the two modules into a hybrid block. In the static detection task, IViT achieves a mAP of 55.3%, a mAP50 of 83.6%, and a mAP75 of 61.0%. For the incremental detection task, it attains a mAP of 57.8%, a mAP50 of 79.7%, and a mAP75 of 62.3%. Extensive experiments on the transmission line corridor external damage dataset and the INSPLAD dataset demonstrate that IViT exhibits outstanding detection performance compared to mainstream static object detection models and incremental object detection models.

The development of a mobile robot that can navigate overhead power transmission lines completely unattended by a human operator is described. Its ultimate purpose is to automate the inspection of power transmission line equipment. The authors have assembled a full-scale functional model of such a mobile robot. Mechanical function testing of the robot has been performed, and results are reported. The robot navigates a ground wire that is located above the actual power transmission lines. It can maneuver over obstructions created by subsidiary equipment on the ground wire such as weights that prevent the wire from twisting and swinging. It is also equipped with an arc-shaped arm that acts as a guide rail and allows it to negotiate transmission towers. When the robot encounters a tower, it unfolds the arc-shaped arm and attaches it to the ground wire on opposite sides of the tower. It then travels along the arm to pass around to the other side of the tower. Once it is firmly affixed to the wire on the other side of the tower it detaches the arm and folds it up until it is needed again.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications.

In order to solve the problems of slow speed, low accuracy and high cost of traditional transmission line inspection methods, intelligent optimization defect identification and optimization strategy for transmission line inspection is proposed. In this paper, through the understanding of the existing problems, construct the inspection plan model, assess the risk level of transmission lines, combined with examples, using MOPSO algorithm to optimize the transmission line inspection plan, to test the feasibility of MOPSO algorithm. Experimental results show that: after optimizing the transmission line inspection plan based on MOPSO algorithm, the inspection interval is smoother, compared with the original inspection plan, the standard deviation of the task interval of the optimization plan is smaller; on the basis of not affecting the inspection effect, the number of inspectors is reduced from the original 20 to 15, which reduces the human resources; with the same amount of inspection work, the cost of the optimization plan is reduced by 12.9% compared with the original plan. Conclusion: MOPSO algorithm based transmission line inspection plan in line with the objective law, the results of the study on the transmission line inspection frequency settings have reference significance, to avoid the traditional method of inspection is not enough or excessive inspection problems, to achieve high efficiency inspection, to obtain the inspection results reflecting the actual operation of the transmission line; inspection plan optimization model to improve the smoothness of the inspection, to ensure the effectiveness of the inspection at the same time to reduce the investment of human resources, high economic efficiency. The MOPSO algorithm is applied with good results, and the research content has guiding significance for the optimization of transmission line inspection plan.

Power systems currently face several challenges in order to address the growing need for sustainable energy worldwide. In this context, nowadays several tasks either directly or indirectly related to power systems can be automated to guarantee their continuous operation. The surveillance and inspection of transmission lines represents a necessary task to meet the energy demands by the commercial and industrial customers. Since power-lines can be thought as hostile environments for humans it is thus necessary to develop new techniques to improve this process, to reduce costs, to improve response times and more important, to avoid operational risks. Although there has been an incipient development of robotic applications in several areas of power systems, it is still a technology under research and development, since it is highly dependent on the nature of the task to be performed and no unified framework of applications exists. This article aims to present the preliminary system for tracking transmission lines based on detection of wires through artificial vision. The system is mounted on a robotic arm, simulating an unmanned aerial vehicle (UAV) which uses the transmission lines detection to position itself. The same system will be used later to inspect the state of the transmission line with the aim of reporting such state and to offer corrective guidelines against damages or problems in the power system if necessary.

Diagnostics of high-voltage power lines using unmanned aerial vehicles enables to identify problems at the early stages. This method of inspection of power transmission lines allows for aerial imaging in hard-to-reach places in order to further analyze the images obtained. The construction of the optimal route in terms of avoiding obstacles and the time spent on calculating the trajectory itself and flying along it to study power lines directly affects the quality, speed, and results of examining the towers and elements of power lines. The paper proposes a method for examining power lines by detection of damage to the structure and elements of power transmission lines. The method consists of two algorithms: an algorithm for detecting operational altitudes and an algorithm for UAV movement along the power transmission line. In our proposed solution, the developed method of inspection of power lines is not limited to one straight section of power lines. The peculiarity of the method is that the path planning using the RRT algorithm along the power transmission line occurs in two-dimensional space at a given working height. To test the efficiency of the proposed survey method, simulation was performed in the Gazebo software environment. The path search time in our proposed survey method does not exceed 0.3 μs for two sections of power transmission lines with an angular tower and a total path length of 200 m. Moreover, this algorithm allows the UAV to fly around the towers at a safe distance, subject to a predetermined terrain map.KeywordsPath planningUAVSurvey of power linesModelingMonitoring of power linesMovement of UAVsMulti-rotor UAVsCollision avoidance

This paper presents a mono-camera based simultaneous obstacle recognition and distance estimation method for power transmission lines(PTLs) inspection robot to avoid obstacles on or around them. The proposed robot inspects the PTLs while moving along them between power transmission towers. For autonomous navigation, our robot recognizes obstacles and avoids a collision with them. In addition, it can stop at the ends of the PTLs by recognizing its structures, such as insulators, installed at the ends of them. In order to recognize obstacles or insulators efficiently, initially, a robust PTLs detection method based on vanishing point estimation is proposed since they are installed on or around the PTLs. Then, obstacle models with various scales are built, and multiple regions-of-interest(ROIs) according to the scales of the model are constructed along the detected PTLs. Finally, obstacles are recognized within the multiple ROIs. Because each ROI represents a corresponding scale of the obstacle model to be matched, the proposed approach can efficiently deal with scale changes and also estimate distance between the robot and the obstacle simultaneously with obstacle recognition. Experimental results not only show that the proposed method correctly recognizes obstacles but also that the distance between the robot and the obstacles is estimated efficiently.

Battery state-of-charge (SOC) estimation is essential for a mobile robot, such as inspection of power transmission lines. It is often estimated using a Kalman filter (KF) under the assumption that the statistical properties of the system and measurement errors are known. Otherwise, the SOC estimation error may be large or even divergent. In this paper, without the requirement of the known statistical properties, a SOC estimation method is proposed using an H∞ observer, which can still guarantee the SOC estimation accuracy in the worst statistical error case. Under the conditions of different currents and temperatures, the effectiveness of the proposed method is verified in the laboratory and field environments. With the comparison of the proposed method and the KF-based one, the experimental results show that the proposed method can still provide accurate SOC estimation when there exist inexact or unknown statistical properties of the errors. The proposed method has been applied successfully to the robot for inspecting the running 500-kV extra high voltage power transmission lines.

This paper presents the structure and configuration of a control system and a videos transmission system of a tribrachiation mobile robot for inspection of power transmission lines. The robot can negotiate obstacles autonomously on the simulated line in the lab. The efficiency and consistency of video transmission is very important remote monitoring and control systems. To enhance the efficiency and consistency, this paper discusses the design process of the remote video transmission and the remote control scheme for inspection robots. It also analyzes the factors that affect consistency of the videos and finds out a method to achieve this function. The efficiency of the video transmission design and the feasibility of the remote control scheme are illustrated by the experiments and simulation results

A two legs mobile robot for inspection of Power Transmission lines

Design and stability performance optimization of a novel hybrid inspection robot walking device for smart grid applications

At present, the method of using unmanned aerial vehicles (UAVs) with traditional navigation equipment for inspection of overhead transmission lines has the limitations of expensive sensors, difficult data processing, and vulnerable to weather and environmental factors, which cannot ensure the safety of UAV and power systems. Therefore, this paper establishes a mathematical model of spatial distribution of transmission lines to study the field strength distribution information around transmission lines. Based on this, research the navigation and positioning algorithm. The data collected by the positioning system are input into the mathematical model to complete the identification, positioning, and safety distance diagnosis of the field source. The detected data and processing results can provide reference for UAV obstacle avoidance navigation and safety warning. The experimental results show that the positioning effect of the positioning navigation algorithm is obvious, and the positioning error is within the range of use error and has good usability and application value.