Transmission Line Inspection Research Articles

A fault detection method for transmission line components based on synthetic dataset and improved YOLOv5

To address insufficient fault images for transmission line components (TLCs) and low accuracy in fault detection using deep learning techniques, we propose a fault detection method for TLCs based on synthetic datasets and improved YOLOv5. First, we introduce a synthetic image approach that uses prior information from the inspection process of a developed flying-walking power transmission line inspection robot (FPTLIR) to generate a synthetic dataset of fault components (SDFC). Second, we propose an improved YOLOv5 network called CSH-YOLOv5 to improve the accuracy of fault detection. The CSH-YOLOv5 network incorporates the convolutional block attention module (CBAM) and the latest SimCSPSPPF module to improve the detection accuracy of the network. In addition, a statistical analysis of small objects in the SDFC is performed and the neck and head of the YOLOv5 network are optimized accordingly to detect small objects. Finally, to address the lack of fault images in the unmanned aerial vehicle (UAV) inspection dataset, we implement a two-stage transfer learning strategy using the SDFC for training. We then experimentally evaluate the performance of the CSH-YOLOv5 network on a real test dataset. The results show that the CSH-YOLOv5 network achieves a mAP@[0.5] of 98.0% and a mAP@[0.5: 0.95] of 64.6% for fault detection, representing an improvement of 7.6% and 8.1%, respectively, over the YOLOv5 network. Comparative analysis indicates that the CSH-YOLOv5 outperforms other popular object detection networks, including Faster-RCNN, YOLOX, and YOLOv7 networks. The two-stage transfer learning strategy employed significantly enhances the network's generalization ability and detection accuracy on the UAV inspection dataset. The proposed method provides a technical reference for fault detection of TLCs, which can potentially benefit the power transmission industry.

Evaluation of the ionization of noble gases under the effect of an electric field for the generation of useful energy

This work originated from the demand presented by an electric power transmission company and addresses a possible solution for the sector by exploring alternatives to extend the flight time of drones in the inspection of transmission lines. This original article demonstrates the use of the electromagnetic field of a transmission line to generate useful electrical power at the terminals of a bulb containing argon gas. It is an unprecedented application in power transmission. In this work, the tests based on a proof of concept are documented, where the results obtained were satisfactory and still allowed to connect an LED through the constructed arrangement. It is observed that the continuity of this research can provide scalability for applications whose main source of ion excitation is given from the energy dissipated as electric field loss in high-voltage lines.

A Novel Vision- and Radar-Based Line Tracking Assistance System for Drone Transmission Line Inspection

This paper introduces a position controller for drone transmission line inspection (TLI) utilizing the integral sliding mode control (SMC) method. The controller, leveraging GNSS and visual deviation data, exhibits high accuracy and robust anti-interference capabilities. A deviation correction strategy is proposed to capture high-voltage transmission line information more robustly and accurately. Lateral position deviation is calculated using microwave radar data, attitude angle data, and deviation pixels derived from transmission line recognition via MobileNetV3. This approach enables accurate and stable tracking of transmission lines in diverse and complex environments. The proposed inspection scheme is validated in settings with 10-kilovolt and 110-kilovolt transmission lines using a drone with a diagonal wheelbase of 0.275 m. The experimental process is available in the YouTube link provided. The validation results affirm the effectiveness and feasibility of the proposed scheme. Notably, the absence of a high-precision positioning system in the validation platform highlights the scheme’s versatility, indicating applicability to various outdoor visual-based tracking scenarios using drones.

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images.

Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.

Research and Application of UAV Nest Dispatching Management System of the Transmission Line

To address the issues of insufficient research and unsatisfactory replacement effects of unmanned aerial vehicle (UAV) hangar for transmission line inspection, a dispatch management system and method for UAV hangar for transmission line inspection are proposed. Combined with multi-UAV hangar control, patrol route planning, unmanned electromechanical energy supplement, remote accurate take-off and landing and other technologies, remote control and centralized dispatching management of UAV hangar are realized, and a new mode of transmission line intelligent patrol operation of UAV hangar is established. In the application practice of transmission line inspection in Foshan area, this system has achieved intelligent inspection of UAV hangar, greatly improving the quality and efficiency of power grid inspection, and providing strong technical support for enhancing the comprehensive ability of power inspection automation and achieving unmanned transmission line inspection.

Simulation Analysis of Obstacle Crossing Stability for Transmission Line Inspection Robot

Simulation Analysis of Obstacle Crossing Stability for Transmission Line Inspection Robot

TTower-345: a multi-categories multi-perspectives benchmark for automatic naming of transmission line inspection photos

TTower-345: a multi-categories multi-perspectives benchmark for automatic naming of transmission line inspection photos

Enhanced Mobility and Hybrid Locomotion in Modular Biped Rolling–Climbing Robots for Power Transmission Line Inspection

Enhanced Mobility and Hybrid Locomotion in Modular Biped Rolling–Climbing Robots for Power Transmission Line Inspection

Retracted: A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems

Retracted: A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems

An efficient multi-order cascade distillation model for the detection of small targets and occluded objects in transmission line inspection

The cascade distillation detection model is proposed to address the problems of low accuracy of multiple small target defect detection and lack of robustness under complex environments in transmission line UAV intelligent inspection. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. Secondly, a high-order aggregated feature extraction backbone network is proposed to enhance the feature extraction of small and medium-sized targets and obscured targets on transmission lines. Then, a spatially deformable attention mechanism is designed to optimize the selection of sampling points for transmission line targets and defects and the calculation of their attention weights by aggregating contextual information in the vicinity of the targets, enhancing the module's ability to refine and adjust the bounding boxes. Finally, a large dataset of transmission line defect targets is constructed and generated, and the validity of the model is verified by experiments.

CSSAdet: Real-Time End-to-End Small Object Detection for Power Transmission Line Inspection

CSSAdet: Real-Time End-to-End Small Object Detection for Power Transmission Line Inspection

Study on The Applicability of Drones in The Process of Forming Towers of 500KV Transmission and Substation Lines in Highland Areas

With the continuous development of science and technology in China, China's aviation field has also been expanding, the rapid development of aviation technology, China has gradually achieved leapfrog development in UAV equipment, and gradually tends to civilization, practicality and commercialization. At present, more and more UAVs are not only opening up in the domestic market, but also have flowed to overseas markets, and the modern advanced technology represented by UAVs has gradually become the current trend development trend. In the field of electric power engineering construction, the use of drone technology is short and wide, and there are drones in both high-altitude areas and plain areas. In terms of the engineering field, the UAV technology involves the construction, monitoring and management of power grids, fault and equipment maintenance of power grids and transmission line inspection, etc.

Semantic Segmentation Algorithm Fusing Infrared and Natural Light Images for Automatic Navigation in Transmission Line Inspection

Unmanned aerial vehicles (UAVs) are widely used in power transmission line inspection nowadays and they need to navigate automatically by recognizing the category and accurate position of transmission pylon equipment in line inspection. Semantic segmentation is an effective method for recognizing transmission pylon equipment. In this paper, a semantic segmentation algorithm that fuses infrared and natural light images is proposed. A cross-modal attention interaction activation mechanism is adopted to fully exploit the complementation between natural light and infrared images. Firstly, a global information block with a feature pyramid structure is used to deeply mine and fuse multi-scale global contextual information of fused features, and then the block is used to conduct feature aggregation in the decoding processing, and enough aggregation with multi-scale features of infrared and natural light images is used to enhance the expression ability of the model and improve the accuracy of semantic segmentation of transmission pylon equipment in complex scenes. Our method guides the process of low-level up-sampling and restoration by denser global and high-level features. Experimental results on a dataset of transmission pylon equipment collected by us show that the proposed method achieved better semantic segmentation results than the state-of-the-art methods.

Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field

Along with the growing size of the power system, the inspection and maintenance of power lines and facilities in complicated environments will be put to a greater test. Currently, more and more research are being done on UAV-based transmission line inspection techniques, of which multi-UAV task assignment and path planning are two important aspects. This paper first generates a three-dimensional inspection environment model while considering and measuring the influence of numerous wind fields in the mountain environment. Next, a multi-indicator hybrid cost function that considers wind field influence is defined for the multi-UAV task assignment problem. An improved Bidirectional Ant Colony and discrete Honey Badger Algorithm (BACOHBA) is proposed to solve it. Then, the multi-UAV path planning problem based on the multi-wind field condition is defined, and an improved Honey Badger-Fruit Fly Algorithm (HBAFOA) is proposed to solve it. Finally, simulation experiments are conducted under different algorithms, which prove that the proposed algorithm has better performance in terms of fast-solving ability, solution accuracy, and optimization stability.

Close-Range Transmission Line Inspection Method for Low-Cost UAV: Design and Implementation

With the rapid development of microelectronics, unmanned aerial vehicles (UAVs) for electric inspection tasks have become popular. Among these tasks, transmission line inspections are more complicated than component and tower inspections owing to the small size, poor functionality, and severe magnetic field interference of transmission lines. Existing solutions invariably use high-precision devices and maintain safety distances during inspections. However, capturing detailed transmission line information over long distances is challenging. Moreover, sophisticated equipment implies high costs and considerable value risks. This work proposes a method using RGB cameras and mm-wave radar to accomplish close-range transmission line inspections. A heading correction and two correction modules address waypoint mission mismatch and wind interference during tracking. In addition, adaptive complementary fusion is designed to solve anomaly identification. Finally, the proposed method validated in a 10 kV transmission line environment demonstrates successful close-range inspection while acquiring high-definition (HD) images. The validation results prove the practical feasibility of the proposed low-cost transmission line inspection method, which is of great significance for reducing inspection costs and promoting the popularization of UAV inspections.

Automated deep learning system for power line inspection image analysis and processing: Architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible

Research on foreign body detection in transmission lines based on a multi-UAV cooperative system and YOLOv7

The unique plateau geographical features and variable weather of Yunnan, China make transmission lines in this region more susceptible to coverage and damage by various foreign bodies compared to flat areas. The mountainous terrain also presents great challenges for inspecting and removing such objects. In order to improve the efficiency and detection accuracy of foreign body inspection of transmission lines, we propose a multi-UAV collaborative system specifically designed for the geographical characteristics of Yunnan's transmission lines in this paper. Additionally, the image data of foreign bodies was augmented, and the YOLOv7 target detection model, which offers a more balanced trade-off between precision and speed, was adopted to improve the accuracy and speed of foreign body detection.

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.

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.

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.