Transmission Line Inspection Research Articles

Research on UAV Three-Phase Transmission Line Tracking and Localization Method Based on Electric Field Sensor Array.

The tracking and positioning of transmission lines is a key element for UAVs (Unmanned Aerial Vehicles) to achieve autonomous inspection of transmission lines. Current methods are vulnerable to weather and environmental factors, have high costs, and have difficulties in data processing. Therefore, this paper proposes a transmission line tracking and localization method based on the electric field sensor array, which calculates the current UAV’s heading angle deflection angle, the distance between the transmission line and the UAV, and the elevation angle, providing a new idea to solve the problem of UAV inspection of transmission lines. At the same time, the electric field distribution of different arrangements of three-phase transmission lines was analyzed using COMSOL to determine the flight area of the UAV. By comparing the electric field distribution of the UAV flight area and single-phase transmission lines, it was verified that the current method is also applicable in the three-phase transmission line scenario, and it was further verified that the sensor array used can sense the change of the UAV position in the flight area, indicating that the electric field sensor array can realize the transmission line tracking and localization of transmission lines. The experimental results showed that, in the three-phase transmission line scenario, when the sensor array moves along the transmission straight wire, the maximum absolute error of the heading angle deflection angle calculated according to this method was 8.2°, the maximum absolute error of the distance between the array and the transmission line was 19.3 cm, and the maximum absolute error of the elevation angle was 11.37°; the error was within a reasonable range and can be used for the UAV to realize autonomous inspection.

Transmission line inspection data visualization system based on 3D GIS

In order to meet the needs of safe and efficient patrol operation and maintenance of modern smart grid, aiming at the poor quality of current transmission line patrol inspection, a design method of transmission line patrol data visualization system based on 3D GIS is proposed. By optimizing the system hardware structure configuration combined with 3D GIS technology, the function and structure of transmission line visual management system are introduced. The accurate collection and processing of transmission line inspection data and the display of inspection route are realized by using three-dimensional GIS technology. Finally, the experiment shows that the transmission line inspection data visualization system based on Three-dimensional GIS has higher practicability and accuracy in the process of practical application, and fully meets the research requirements.

InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection

Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images.

Multi-Tour Set Traveling Salesman Problem in Planning Power Transmission Line Inspection

This letter concerns optimal power transmission line inspection formulated as a proposed generalization of the traveling salesman problem for a multi-route one-depot scenario. The problem is formulated for an inspection vehicle with a limited travel budget. Therefore, the solution can be composed of multiple runs to provide full coverage of the given power lines. Besides, the solution indicates how many vehicles can perform the inspection in a single run. The optimal solution of the problem is solved by the proposed Integer Linear Programming (ILP) formulation, which is, however, very computationally demanding. Therefore, the computational requirements are addressed by the combinatorial metaheuristic. The employed greedy randomized adaptive search procedure is significantly less demanding while providing competitive solutions and scales better with the problem size than the ILP-based approach. The proposed formulation and algorithms are demonstrated in a real-world scenario to inspect power line segments at the electrical substation.

Mathematical Model and Attitude Estimation Using Extended Colored Kalman Filter for Transmission Lines Inspection's Unmanned Aerial Vehicle

Mathematical Model and Attitude Estimation Using Extended Colored Kalman Filter for Transmission Lines Inspection's Unmanned Aerial Vehicle

Slippage fault diagnosis of dampers for transmission lines based on faster R-CNN and distance constraint

The slippage fault diagnosis of dampers in aerial images is a challenging task for an automatic transmission line inspection system. In this paper, an effective and reliable slippage fault diagnosis method based on a deep learning technique and distance constraint method for aerial images is proposed. In the detection stage, the Faster Regions with Convolutional Neural Network (R-CNN) is employed. It takes the aerial images as input, detects the dampers in the image by learning the basic features of dampers, and outputs the coordinates of the dampers in a single image. In the diagnosis stage, the distance between adjacent dampers in the horizontal axis is calculated through the damper coordinates obtained in the detection stage, and the relationship between the distance and the selected threshold is the judgment basis of slippage fault diagnosis. The results show that the slippage fault of dampers can be quickly and accurately diagnosed in the aerial images by the proposed method.

Unmanned Aerial Vehicle for Transmission Line Inspection: Status, Standardization, and Perspectives

As the biggest country in power consumption and generation, China owns the largest scale of transmission line worldwide. The application of unmanned aerial vehicle (UAV) in inspection has been carried out for many years in China because of its strong adaptability and safety compared to the traditional manual way. For the further development of UAV application technology in transmission line inspection, China has accumulated rich experiences and greatly improved line inspection efficiency and formed a relatively complete UAV transmission line inspection standardization system including operation safety management, an equipment configuration method, and functional quality requirements. However, there still exist some obstacles that need to be tackled due to the fact that China’s UAV transmission line inspection standardization and its international promotion have not been thoroughly and comprehensively studied at present. Based on an in-depth analysis of China’s current UAV transmission line inspection standardization system, this study employs SWOT-PEST analysis to insightfully investigate international promotion opportunities/challenges under the current situation, upon which several constructive suggestions are proposed from four aspects of politics, economy, society, and technology. The ultimate purpose is to design feasible schemes to boost the international promotion of China’s UAV transmission line inspection standardization and further development of international UAV inspection technology.

FWMR-II: a modular link-type rope-climbing robot with the finger-wheeled mechanism

Purpose The rope-climbing robot that can cling to a rope for locomotion has been a popular piece of equipment for some overhead applications due to its high flexibility. In view of problems left by existing rope-climbing robots, this paper aims to propose a new-style rope-climbing robot named Finger-wheeled mechanism robot (FWMR)-II to improve their performance. Design/methodology/approach FWMR-II adopts a modular and link-type mechanical structure. With the finger-wheeled mechanism (FWM) module, the robot can achieve smooth and quick locomotion and good capability of obstacle-crossing on the rope and with the link module based on a spatial parallel mechanism, the robot adaptability for rope environments is improved further. The kinematic models that can present configurations of the FWM module and link module of the robot are established and for typical states of the obstacle-crossing process, the geometric definitions and constraints that can present the robot position relative to the rope are established. The simulation is performed with the optimization calculating method to obtain the robot adaptability for rope environments and the experiment is also conducted with the developed prototype to verify the robot performance. Findings From the simulation results, the adaptability for rope environments of FWMR-II are obtained and the advantage of FWMR-II compared with FWMR-I is also proved. The experiment results give a further verification for the robot design and analysis work. Practical implications The robot proposed in this study can be used for inspection of power transmission lines, inspection and delivery in mine and some other overhead applications. Originality/value An ingenious modular link-type robot is proposed to improve existing rope-climbing robots and the method established in this study is worthy of reference for obstacle-crossing analysis of other rope-climbing robots.

Approach on Recognizing Uncovered Earth Region from Aerial Images Based on Multi-feature Fusion

It is one of the main hidden dangers of power transmission lines accidents if there is the uncovered earth under or near power transmission lines. It can give the important early warning message to prevent the accidents through recognizing the uncovered earth region from aerial images of Unmanned Aerial Vehicle (UAV) power transmission lines inspection. Due to the low recognizing precision of Mask RCNN CNN (Mask Convolution Neural Network), this paper proposed an approach to recognize the uncovered earth region from aerial images of UAV by image feature fusion. The HOG and LBP features of aerial images were extracted and their dimension were reduced. Then these two features were fused by different weights. The experiments show that, (1) the average precision of recognizing the uncovered earth region can be above 80%, which is the lowest requirement to use; (2) the weights of two features should make the orders of magnitude of the two features as equal as possible. The approach is application for the first image filtering by the UAV airborne platform because it not only has enough good recognizing precision but also is very rapid, which provides a novel way for objective recognition from UAV aerial images.

Transmission line image mosaic based on improved SIFT-LATCH

In the transmission line inspection, in order to obtain wide-view and large-scale images, it is necessary to stitch images taken by drones. This article proposes an improved SIFT-LATCH-based UAV aerial transmission line image stitching algorithm. First, the image is processed in parallel through multi-threading. The SIFT algorithm is used to extract the feature points in the image, and the LATCH descriptor is used to describe the feature points. The sampling consensus algorithm is used to purify the feature points, and finally the translational homography matrix is used to solve the problem of black lines in the stitched image. Experiments show that the algorithm proposed in the article has the advantages of good real-time and high accuracy in splicing UAV aerial pictures of transmission lines.

SA RCNN for Occluded Damper Detection in the Inspection of Power Transmission Line

Dampers can prevent wires from breaking due to vibration, so it plays an important role in ensuring the stable operation of transmission lines. But the accuracy of popular object detection algorithm is still very low, because the occlusion phenomenon of dampers is serious due to the variable shooting angle. In this paper, we introduce our Supervised Attention RCNN (SA RCNN) to improve the detection effect of occluded dampers. SA RCNN mainly consists of two components: Box Supervised Attention Module (BSAM) and One Proposal Multiple Predictions (OPMP). In order to solve the problem of false detection caused by similarity of features, BSAM enhances features by using ground truths as supervised information to guide the generation of attention. In order to solve the problem of missed detection due to the NMS strategy, OPMP predicts a set of instances for one proposal rather than a single one. Experiments show that SA RCNN obtains 4.2% AP improvements on the dataset of occluded dampers compared to FPN.

Research on Real-time Tree Barrier Measurement System Based on Three Dimensional Lidar

Removing dangerous tree barrier is an important condition to ensure the safe operation of transmission line. A tree barrier measurement technology based on Unmanned Aerial Vehicle (UAV) is proposed in this paper. The technology detects the tree barrier near the transmission line by carrying Three Dimensional (3D) Lidar and on-board computer, de-noises the data obtained during the scanning process, aggregates and classifies the tree barrier data model. Combined with tree barrier measurement algorithm, a real-time tree barrier measurement system and method are formed. Field experiments show that the system can effectively improve the accuracy and efficiency of traverse tree barrier measurement, which is conducive to the realization of information, intelligence and safety of transmission line inspection, and has practical application value for the safe operation of the line.

Algorithm of object localization applied on high-voltage power transmission lines based on line stereo matching

The algorithm designing of object localization is one of the key technologies in the research field of high-voltage power transmission line inspection robot. We present an obstacle localization method based on line stereo matching to solve the problems remaining in the current obstacle localization algorithms such as the weak capacity of resisting distraction, the low localization precision, and the extra prior conditions. The proposed algorithm regards line as the element unit for stereo matching and adopts Earth mover’s distance as similarity measurement criterion. In addition, it can remove false matching points through the condition of distance constraint and the distribution information of depth. Finally, we give all sampling points on obstacle different weights according to the distance between the points and obstacle center, through which the weighted mean depth of obstacle is obtained. The experimental result shows that the mean relative localization error is 2.697% and the fluctuating range is 1.11% to 3.58%, which means the localization precision is high and the error fluctuation is small. So this algorithm can meet the demand of obstacle localization for high-voltage power transmission lines inspection robot efficiently.

A Method for Autonomous Navigation and Positioning of UAV Based on Electric Field Array Detection.

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.

송전선로 드론점검을 위한 딥러닝 기반 자동촬영 카메라짐벌 시스템 개발

In this paper, a new automatic camera gimbal system was developed to efficiently inspect transmission lines using a drone. As drone technology has advanced tremendously over the past few years, drones are being used to replace dangerous tasks in the electric power industry. Especially, Korea Electric Power Corporation has used the automatic drone inspection system developed for power transmission lines, but while this drone system flies on autopilot, its camera gimbal is still controlled manually. Moreover, the camera gimbal control in the field was often interrupted by electromagnetic interference from ultra-high voltage power lines. To overcome this problem, we developed the new camera gimbal system that can automatically shoot power facilities on the basis of Deep Learning. Its control algorithms mainly consists of a photographing algorithm for a steel tower, a photographing algorithm for power conductors, and an automatic drone landing algorithm. This fully automated system is expected to greatly increase the task efficiency and accuracy for transmission line inspection without any kinds control efforts.

Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.

Automatic Insulator Detection for Power Line Using Aerial Images Powered by Convolutional Neural Networks

The inspection and fault diagnosis of power transmission line via aerial images is becoming widely used in smart grids and electrical power systems. However, the accuracy of detecting objects with complicated backgrounds still remains an important challenge in the defect detection system. In this article, a deep-learning based method is presented to realize the automation of insulator fault detection. In the proposed approach, the insulator localization network based on Single Shot Multibox Detector (SSD), a one-stage detecting backbone, is utilized to extract multi-level features and make predictions in order to determine the insulator situation in the aerial images. Additionally, Densely Connected Convolutional Networks (DenseNet) is newly introduced to strengthen the insulator detecting system’s classification ability. To address the data scarcity problem, a two-stage data augmenting strategy is also applied. The first stage is mainly based on the image combination, while the second stage includes random affine transformation, Gaussian blur, brightness and contrast transformation, and salt-and-pepper noise. The insulator detection network and the classification network precisions of the proposed method have reached 0.95 and 0.98 using the text dataset, respectively. The results have shown that the proposed method can ensure robustness and accuracy during insulator detection.

Obstacle Distance Measurement Under Varying Illumination Conditions Based on Monocular Vision Using a Cable Inspection Robot

Obstacle distance measurement is one of the key technologies for autonomous navigation of high-voltage transmission line inspection robots. To address the robustness of obstacle distance measurement under varying illumination conditions, this article develops a research method that fuses image enhancement with robot monocular vision so that the robot can adapt to various levels of illumination running along the transmission line. During the inspection of high-voltage transmission lines in such an overexposed (excessively bright) environment, a specular highlight suppression method is proposed to suppress the specular reflections in an image; when scene illumination is insufficient, a robust low-light image enhancement method based on a tone mapping algorithm with weighted guided filtering is presented. Based on the monocular vision measurement principle, the error generation mechanism is analyzed through experiments, and we introduce the parameter modification mechanism. The two proposed image enhancement methods outperform other state-of-the-art enhancement algorithms in qualitative and quantitative analyses. The experimental results show that the measurement error is less than 3% for static distance measurements and less than 5% for dynamic distance measurements within 6 m. The proposed method can meet the requirements of high-accuracy positioning, real-time performance and strong robustness. This method greatly contributes to the sustainable development of inspection robots in the power industry.

Route Optimization Method of UAV Autonomous Transmission Line Detection Based on Beidou Positioning Service

By using Beidou high-precision positioning technology, drone autonomous flight technology, and drone communication technology, a drone inspection system has been constructed. In order to further improve the work efficiency of UAV inspection on transmission lines, a single-base tower UAV inspection track optimization method based on the filtering method of the sphere unit limit is proposed. With the assistance of precision positioning services, the autonomous drone inspection hovering accuracy can reach an average of 5cm (E-W), 6cm (N-S), and 7cm (H), which meets the requirements of fine line inspections. The method proposed in this paper can further improve the efficiency of UAV fine independent inspection while improving the intelligent level of transmission line inspection.

Environment Perception Technologies for Power Transmission Line Inspection Robots

With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three‐dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR‐based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning‐based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.