Line Inspection Research Articles

Cluster control technology of transmission line intelligent inspection drones based on 5G communication

Abstract This paper firstly analyzes the network topology model of the UAV cluster network and wireless 5G communication channel model by modeling and briefly analyzes the idea of topology movement control for flying self-organized networks. Then, a cluster-based structure and reinforcement learning clustered routing protocol is proposed for the problem of easy breakage of routing forwarding paths caused by smart inspection of transmission lines based on UAV clusters for 5G communication. Finally, a cluster structure-based precedence routing protocol is designed, an adaptive routing protocol based on location and link quality Q-learning is used between clusters, and fast and reliable routing is achieved by combining the routing table maintained by itself. The simulation results show that ARP-L-Q (average end-to-end delay 4.22, average packet loss rate 88.09%, average packet rate 2.37, average control overhead 2.52) protocol performs better than GPSR and GACB protocols, and the experiment verifies that ARP-L-Q protocol can better achieve the high dynamic reconfiguration, high stability and reliability, and low communication delay of UAV cluster-based 5G communication network. Characteristics and requirements. This study has application prospects in both civil emergency and military mobile communication and has certain military significance, theoretical value and application value for thus promoting UAV innovation.

Cooperative control method of transmission line inspection UAV cluster based on hybrid networking technology

Abstract With the progress of technology, inspection UAV clusters oriented to collaborative control are increasingly widely used in electric power inspection with the advantages of information sharing, task collaboration and multiplication of effectiveness. This paper proposes a hybrid cluster access selection algorithm for transmission line (QS) assurance of electric power business based on analyzing the differentiated needs of the electric power business. The entropy power method calculates objective weights for transmission line inspection, and the game theory is used to fuse the subjective and objective weights to determine the comprehensive weights. Secondly, the cooperative control rate is designed for the power inspection UAV cluster, and a set of cooperative control management systems for the power inspection UAV cluster is designed and implemented through detailed requirement analysis. The results show that the cooperative control of UAV cluster based on hybrid networking MAC protocol reduces the average time of path planning by 36.08s, increases the average path length by 7.30m, and reduces the average number of sampling points by 21.4% compared with RRT algorithm. The transmission line inspection UAV cluster cooperative control proposed in this paper can effectively and quickly detect faults on transmission lines and maximize the network utility function value, thus providing the optimal network access selection scheme for each power transmission.

Application Improvement of Deep Learning Algorithm in Small-Sized Fittings, Voltage Balancing Ring and Bare Conductor Detection of Transmission Lines

As technological advancements progress and energy conservation and emission reduction policies gain traction, an increasing amount of clean energy is being integrated into the power grid system. This influx of new energy imposes stringent demands on the transmission lines within the power grid system. In recent years, the State Grid has implemented a plethora of intelligent transmission line inspection strategies, with the intelligent inspection of Unmanned Aerial Vehicle (UAV) transmission lines receiving significant promotion and widespread application. However, practical application has revealed that the prevalent transmission line detection algorithms yield a substantial quantity of false detections, particularly in the detection of nut defects in small-sized metallic fittings, voltage balancing ring defects, and defects in uninsulated conductors. To address these issues, this paper employs deep learning algorithms for target detection, critical point detection, and instance segmentation, focusing on aspects such as algorithmic logic, algorithmic models, and data processing. The aim is to enhance the precision of these three types of defect detection, diminish the rate of false detections, and augment the practicality of intelligent grid inspection.

Auto-Inspection System Using Optimized Fuzzy Sliding Mode Control Strategy for Tunnel Inspection

Cities composed of many mountainous areas necessitate the use of many tunnels for roads and highways with a potential safety hazard. To determine the safety of tunnels, periodic tunnel inspections mainly rely on manual work, which is dangerous and slow. Therefore, this paper proposes an auto-inspection system for tunnel inspection consisting of a robotic arm, laser sensors, and an inspection radar to free inspectors from hazardous environments and high-intensity work. Based on the mathematical model in the inspection process, a sliding mode controller is designed and optimized with fuzzy control and hyperbolic tangent functions, and is used in a tunnel inspection robot system for the first time. The simulation results show that optimized fuzzy sliding mode control can improve the tracking accuracy and stability during the tracking process compared with the traditional algorithm. Curved line inspection and arch line inspection experiment tests demonstrate that our system can automatically inspect the tunnel, and that the optimized fuzzy sliding mode control provides a superior performance in terms of the tracking process, with the average error decreasing by 37.8% when compared to traditional algorithms. Therefore, the proposed system is of great significance for high-precision and high-stability unmanned automated tunnel inspection.

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.

Research on Speed Control Methods and Energy-Saving for High-Voltage Transmission Line Inspection Robots along Cable Downhill

To ensure the safe operation of high-voltage transmission line inspection robots during downhill descents without power and extend their range after a single charge, this paper proposes an energy-saving speed control method for the inspection robot’s walking wheel motor on downhill slopes by integrating feedback braking and fuzzy PID control. By combining the parameter equation of the overhead catenary line and the structural characteristics of the overhead transmission line, this paper analyzes the relationship between the driving torque of the inspection robot’s wheels and the horizontal displacement along the transmission ground wire before and after descending. Based on this analysis, a speed control and energy recovery scheme is developed for the inspection robot, which combines front-wheel feedback braking with rear-wheel regenerative braking. The fuzzy PID method is utilized to adjust the PWM duty cycle to achieve energy-efficient speed control of the inspection robot’s rear walking wheels. Additionally, to improve the energy density and specific power of the robot’s energy storage unit, a composite power source consisting of lithium batteries and supercapacitors is employed to recover energy from the front walking wheels through feedback braking. The combined simulation results indicate that, compared to fuzzy control and PID control, fuzzy PID control better regulates the robot’s speed under varying slopes, wind resistance, and cable roughness. A downhill speed control system for the inspection of the robot’s walking wheel motor was designed, and its effectiveness was validated through simulated high-voltage line experiments. The fuzzy PID control was demonstrated to effectively maintain the rear walking wheel speed within the targeted range during downhill descents. When descending along a fixed 30° angle cable, the fuzzy PID control resulted in an increase of 5.28% and 14.26% in the state of charge (SOC) of the supercapacitor compared to PID control and fuzzy control, respectively. Moreover, when descending along fixed angle cables of 10°, 20°, and 30°, as well as a variable angle cable ranging from 30° to 0°, the SOC of the supercapacitor increased by 17.55%, 26.25%, 38.45%, and 31.29%, respectively. This demonstrates the effective absorption of regenerative braking energy during the robot’s downhill movement.

A Lightweight Insulator Detection Methodology for UAVs in Power Line Inspection

The defect of insulators on the power line may lead to the failure of the electric transmission system, which in turn endangers the safe and reliable operation of the whole power system. Therefore, timely and accurate detection of insulators has become a current research hotspot. However, manual inspection cannot guarantee real-time detection and is prone to security accidents. Thanks to the convenience of Unmanned Aerial Vehicles (UAVs), collecting insulator images by UAVs and designing detection algorithms on insulators for real-time and accurate insulator detection have become a practical option. In this paper, considering the poor computing resources of UAVs, a lightweight object detection algorithm, NanoDet, is trained to detect insulators. In addition, to fully utilize the computing resources, the proposed model is segmented. Power-efficient layers are mapped to the central processing units (CPUs) for execution. Experimental results indicate that the proposed model can cut down the power consumption by up to 46.4% without violating the time constraint.

Component Detection for Power Line Inspection Using a Graph-Based Relation Guiding Network

Detecting the components in aerial images is a crucial task in automatic visual inspection for power lines. Currently, deep learning models guided by external knowledge have achieved promising performances compared to directly applying the benchmark detectors. However, the component relationship, as human commonsense knowledge for object reasoning, is rarely investigated in this field. This study presents a graph-based relation guided network for power line component detection, which exploits correlations of regions, images, and categories. The visual relation module is employed to learn region-to-region relationship and enhance the visual features of each proposal that may contain components. Meanwhile, two guidance modules are proposed to capture image-to-region correlation and distinctively facilitate the category classification and position regression, which has not been considered in previous methods. Moreover, the category graphs built in these two modules are able to explore category-to-category dependencies that can further promote the network ability. Experimental results demonstrate that the proposed method can achieve more accurate and reasonable component detection compared to previous methods, which verifies the effectiveness of the proposed model incorporated with relation knowledge.

Research on 3D image reconstruction of sparse power lines by array GM-APD lidar

Power lines and other tiny obstacles in the air are deadly hazards to safe aircraft flight. However, existing detection methods have difficulty sensing power lines’ weak radiation signals submerged in noise, making power lines detection an international research hotspot. To solve this problem, this paper proposes the use of array Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (lidar) with single photon detection sensitivity to achieve remote three-dimensional (3D) imaging detection of power lines (GM-PLTR). We focus on solving the problem of noise filtering for sparse power lines under low Signal-to-noise ratio (SNR) conditions and completing 3D image reconstruction. Firstly, we propose a method based on adaptive thresholding and waveform density discrimination combined with spatial dependency to remove the pixel-wise noise, including environmental light, noise echoes from neighboring pixels, and dark count noise. Then we obtain the high SNR depth image of the targets. Secondly, we use an improved DBSCAN algorithm to separate the power lines from the background noise in the depth image to improve the reconstruction effect. Finally, we use a one-directional template matching method to fill in the missing shapes of the power lines. Experimental results show that the algorithm improves the SNR of the power lines echo signal by an average of 2.3 times and achieves sparse power lines imaging detection at 340.95 m. Moreover, the Hough transform confirms the possibility of high-precision detection of power lines, significantly reducing the difficulty of detecting small targets. Finally, it will help unmanned aerial vehicles (UAVs) complete the inspection of power lines.

A Novel Autonomous Landing Method for Flying–Walking Power Line Inspection Robots Based on Prior Structure Data

Hybrid inspection robots have been attracting increasing interest in recent years, and are suitable for inspecting long-distance overhead power transmission lines (OPTLs), combining the advantages of flying robots (e.g., UAVs) and climbing robots (e.g., multiple-arm robots). Due to the complex work conditions (e.g., power line slopes, complex backgrounds, wind interference), landing on OPTL is one of the most difficult challenges faced by hybrid inspection robots. To address this problem, this study proposes a novel autonomous landing method for a developed flying–walking power line inspection robot (FPLIR) based on prior structure data. The proposed method includes three main steps: (1) A color image of the target power line is segmented using a real-time semantic segmentation network, fusing the depth image to estimate the position of the power line. (2) The safe landing area (SLA) is determined using prior structure data, applying the trajectory planning method with geometric constraints to generate the dynamic landing trajectory. (3) The landing trajectory is tracked using real-time model predictive control (MPC), controlling FPLIR to land on the OPTL. The feasibility of the proposed method was verified in the ROS Gazebo environment. The RMSE values of the position along three axes were 0.1205,0.0976 and 0.0953, respectively, while the RMSE values of the velocity along these axes were 0.0426, 0.0345 and 0.0781. Additionally, experiments in a real environment using FPLIR were performed to verify the validity of the proposed method. The experimental results showed that the errors of position and velocity for the FPLIR landing on the lines were 6.18×10−2 m and 2.16×10−2 m/s. The simulation results as well as the experimental findings both satisfy the practical requirements. The proposed method provides a foundation for the intelligent inspection of OPTL in the future.

Communication via quad/hexa-copters during disasters

PurposeThe purpose is to derive the most effective place in the air for an aerial robot, viz., drone to use as an alternative communication system during disasters.Design/methodology/approachIn this technology-driven era, various concepts are becoming the area of interest for multiple researchers. Drone technology is also one of them. The researchers, with interest in drones, are therefore trying to understand the various uses of employing drones in diverse applications which are mind-boggling, starting from civil applications (viz., an inspection of power lines, counting wildlife, delivering medical supplies to inaccessible regions, forest fire detection, and landslide measurement) to military applications (viz., real-time monitoring, surveillance, patrolling, and demining). However, one area where its usage is still to be exploited in many countries is using drones as a relay when communication lines are disrupted due to natural calamities. This will be particularly helpful in rescuing the affected people as the aerial node will enable them to communicate to the rescue team using mobiles/ordinary landline telephones even when regular communication towers are destroyed due to disastrous natural calamities, for example, tsunamis, earthquakes, and floods. Various algorithms, namely, water filling algorithm, advanced water filling algorithm, equal power distribution algorithm, and particle swarm optimization, were therefore studied and analyzed using simulation in addition to various path loss models to realize the desired place for an aerial robot, viz., drone in the air, which will eventually be used as an alternative communication system for badly hit ground users due to any disaster.FindingsIt was found that the effective combination of the water filling algorithm and particle swarm optimization algorithm may be done to place the drone in the air to increase the overall throughput of the affected ground users.Originality/valueThe research is original. None of the parts of this research paper has been published anywhere.

An improved block-level approach for tunnel lining crack detection

Abstract Tunnel lining cracks pose a great safety risk to safe operation. Due to severe illumination conditions and lining cracks, which are usually thinner with varied shapes compared to cracks generated in pavement, bridge, and other transportation infrastructure, the lining crack inspection at the block level is a challenging task. In this study, to address this issue, we propose a novel Swin Transformer-based network including Shape-IoU method to enhance the perception of the tunnel lining crack and the refinement of the crack. To validate the superiority and robustness of the proposed model, a total of six classic object detection models (i.e. Cascade Mask R-CNN, Cascade R-CNN, Faster R-CNN, feature selective anchor-free module, fully convolutional one-stage object detection, neural architecture search—fully convolutional one-stage object detection) are adopted for model training, validation, and testing with the customized inspection dataset, which includes 1,200 high resolution tunnel lining inspection images. With the training results and the visualization results, our proposed model has shown excellent performance across multiple metrics. Specifically, the enhanced Swin Transformer model achieves a remarkable 96.10 per cent on the mAP50 metric, surpassing the original model by 0.80 per cent. Moreover, it exhibits an accelerated detection speed of 2.4 tasks/s compared to the previous rate of 1.43 tasks/s. The results demonstrate that the proposed methodology in this paper significantly enhances both detection accuracy and speed of the model, paving the way for field application in the near future.

Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure.

The power sector is one of the most important engineering sectors, with a lot of equipment that needs to be appropriately maintained, often spread over large areas. With the recent advances in deep learning techniques, many applications can be developed that could be used to automate the power line inspection process, replacing previously manual activities. However, in addition to these novel algorithms, this approach requires specialized datasets, collections that have been properly curated and labeled with the help of experts in the field. When it comes to visual inspection processes, these data are mainly images of various types. This paper consists of two main parts. The first one presents information about datasets used in machine learning, especially deep learning. The need to create domain datasets is justified using the example of the collection of data on power infrastructure objects, and the selected repositories of different collections are compared. In addition, selected collections of digital image data are characterized in more detail. The latter part of the review also discusses the use of an original dataset containing 2630 high-resolution labeled images of power line insulators and comments on the potential applications of this collection.

TLI-YOLOv5: A Lightweight Object Detection Framework for Transmission Line Inspection by Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) have become an important tool for transmission line inspection, and the inspection images taken by UAVs often contain complex backgrounds and many types of targets, which poses many challenges to object detection algorithms. In this paper, we propose a lightweight object detection framework, TLI-YOLOv5, for transmission line inspection tasks. Firstly, we incorporate the parameter-free attention module SimAM into the YOLOv5 network. This integration enhances the network’s feature extraction capabilities, without introducing additional parameters. Secondly, we introduce the Wise-IoU (WIoU) loss function to evaluate the quality of anchor boxes and allocate various gradient gains to them, aiming to improve network performance and generalization capabilities. Furthermore, we employ transfer learning and cosine learning rate decay to further enhance the model’s performance. The experimental evaluations performed on our UAV transmission line inspection dataset reveal that, in comparison to the original YOLOv5n, TLI-YOLOv5 increases precision by 0.40%, recall by 4.01%, F1 score by 1.69%, mean average precision at 50% IoU (mAP50) by 2.91%, and mean average precision from 50% to 95% IoU (mAP50-95) by 0.74%, while maintaining a recognition speed of 76.1 frames per second and model size of only 4.15 MB, exhibiting attributes such as small size, high speed, and ease of deployment. With these advantages, TLI-YOLOv5 proves more adept at meeting the requirements of modern, large-scale transmission line inspection operations, providing a reliable, efficient solution for such demanding tasks.

Benchmarking Generations of You Only Look Once Architectures for Detection of Defective and Normal Long Rod Insulators

Effective infrastructure monitoring is a priority in all technical fields in this century. In high-voltage transmission networks, line inspection is one such task. Fault detection of insulators is crucial, and object detection algorithms can handle this problem. This work presents a comparison of You Only Look Once architectures. The different subtypes of the last three generations (v3, v4, and v5) are compared in terms of losses, precision, recall, and mean average precision on an open-source, augmented dataset of normal and defective insulators from the State Grid Corporation of China. The primary focus of this work is a comprehensive subtype analysis, providing a useful resource for academics and industry professionals involved in insulator detection and surveillance projects. This study aims to enhance the monitoring of insulator health and maintenance for industries relying on power grid stability. YOLOv5 subtypes are found to be the most suitable for this computer vision task, considering their mean average precision, which ranges between 98.1 and 99.0%, and a frame per second rate between 27.1 and 212.8, depending on the architecture size. While their predecessors are faster, they are less accurate. It is also discovered that, for all generations, normal-sized and large architectures generally demonstrate better accuracy. However, small architectures are noted for their significantly faster processing speeds.

Deep-Learning-Based Detection of Transmission Line Insulators

At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning detection algorithms with the UAV transmission line inspection system can effectively solve the current sensing problem. To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. Compared with Faster R-CNN, Retina Net, and other detection algorithms, the model is highly accurate and can effectively deal with the false detection, leakage, and non-recognition of the environment in online special detection. The research in this paper provides a new idea for intelligent fault detection of transmission line insulators and has some reference value for engineering applications.

Rate-accuracy optimized quantization algorithm based on ROI image coding in power line inspection

Rate-accuracy optimized quantization algorithm based on ROI image coding in power line inspection

Measuring defects in high-speed production lines—a three-phase convolutional neural network model

Conventional automatic optical inspection (AOI) systems using rule-based image processing suffer from precision and velocity issues, particularly when simultaneously measuring multiple defects of a product in a high-speed production line. Such AOI stations usually become a bottleneck in the line. This paper presents a three-phase model for defect detection based on convolutional neural network to release the cycle time of the line. The phase I model using a deep residual network (ResNet50) performs defect classification of products with high accuracy. The phase II model is another independent ResNet50 that classifies defect products into defect categories, rescuing good products that have been mistakenly killed in the previous stage and replacing the conventional re-inspection labors. The phase III model is a you only look once—based network that detects multiple defects and their positions simultaneously in a single product, providing informative quality data for continuous improvement. The proposed model successfully resolves the issue of multiple-defect and multiple-len quality inspection in a high-speed production line. The proposed model resolved defect inspection by integrating object detection and defect classification simultaneously. By deploying the three-phase model in a tiny electronic connector component production line, the present model has demonstrated that it reaches high precision and facilitates prompt quality correction for high-speed production lines.

Research and Application of the Obstacle Avoidance System for High-Speed Railway Tunnel Lining Inspection Train Based on Integrated 3D LiDAR and 2D Camera Machine Vision Technology

This study presents an innovative, intelligent obstacle avoidance module intended to significantly enhance the collision prevention capabilities of the robotic arm mechanism onboard a high-speed rail tunnel lining inspection train. The proposed module employs a fusion of ORB-SLAM3 and Normal Distribution Transform (NDT) point cloud registration techniques to achieve real-time point cloud densification, ensuring reliable detection of small-volume targets. By leveraging spatial filtering, cluster computation, and feature extraction, precise obstacle localization information is further obtained. A fusion of multi-modal data is achieved by jointly calibrating 3D LiDAR and camera images. Upon validation through field testing, it is demonstrated that the module can effectively detect obstacles with a minimum diameter of 0.5 cm, with an average deviation controlled within a 1–2 cm range and a safety margin of 3 cm, effectively preventing collisions. Compared to traditional obstacle avoidance sensors, this module provides information across more dimensions, offering robust support for the construction of powerful automated tunnel inspection control systems and digital twin lifecycle analysis techniques for railway tunnels.

Comparison of methods for monitoring icing of high-voltage power lines and an overview of measuring equipment used to diagnose such lines

The low structural reliability of overhead lines, due to continuous climatic influence, becomes the cause of electrical network accidents and unreliable power supply. Ensuring the trouble-free operation of overhead lines is a complex task, the solution of which is of the utmost importance for the normal functioning of the entire infrastructure. In the autumn-winter period, difficult weather conditions almost completely exclude the quick completion of repair works. A fairly large share of accidents occurs during the formation of icy deposits on wires and lightning protection cables. Today, visual inspections of high-voltage power lines remain the main way of diagnosing icy deposits. In practice, it is necessary to equip overhead lines with ice load telemetry devices, which will allow monitoring the formation of ice deposits on wires in real time. The following diagnostic methods are used in modern ice monitoring devices: strainometric, fiber-optic, aerodynamic, locational, and instrumental-parametric. The tensiometric method of diagnosis is implemented by means of direct measurement of the icy load on the wire with subsequent comparison of the measured values with predetermined values of the threshold loads. To implement this diagnostic method, magnetoelastic force measuring sensors are used. Such a sensor is connected between the traverse of the U-shaped support and the upper end of the corresponding garland of insulators with a phase wire. Ice, wind and ice-wind loads are measured separately. This method has a limited scope of application, namely for diagnosing the formation of ice on the phase wires of intermediate spans of single-circuit lines with two-post U-shaped resistances. The fiber-optic method of strain gauge measurements is implemented in object control systems under difficult operating conditions, at nuclear power plants. The mechanical effort sensor (fiber-optic strain sensor) implements the fiber-optic method of strain measurement. Fiber-optic strain sensors are characterized by high accuracy due to resistance to interference and complexity of execution.