Power Transmission Line Inspection Research Articles

Optimizing 2D path planning for unmanned aerial vehicle inspection of electric transmission lines

The inspection of electric power transmission lines using unmanned aerial vehicles represents an innovative maintenance approach that several countries, including Morocco, are considering adopting. The primary aim is to enhance existing procedures, capitalize on their advantages, and address challenges associated with traditional methods. Given the precision, efficiency, and safety required for this application to ensure reliable results, we propose a functional architecture for unmanned aerial vehicle-based electric power transmission line inspection. This architecture encompasses key phases such as data collection, planning, and inspection, while identifying electromagnetic interferences and minimum approach distances as major constraints. To streamline mission planning, we introduce a trajectory optimization algorithm integrated into a mission planning assistance application for transmission line inspection. This algorithm presents a novel approach by hybridizing three algorithms: genetic algorithm, ant colony optimization and particle swarm optimization. Consequently, our work represents a significant contribution to the integration of unmanned aerial vehicles in electric power transmission lines inspection operations for electrical energy transport, facilitating potential advancements and discoveries in the field.

Multiobjective Energy Consumption Optimization of a Flying–Walking Power Transmission Line Inspection Robot during Flight Missions Using Improved NSGA-II

In order to improve the flight efficiency of a flying–walking power transmission line inspection robot (FPTLIR) during flight missions, an accurate energy consumption model is constructed, and a multiobjective optimization approach using the improved NSGA-II is proposed to address the high energy consumption and long execution time. The energy consumption model is derived from the FPTLIR kinematics to the motor dynamics, with the key parameters validated using a test platform. A multiobjective optimization model is proposed that considers many constraints related to the FPTLIR during missions, offering a comprehensive analysis of the energy consumption and execution time. The NSGA-II algorithm is improved by integrating the Cauchy variation operator and the simulated annealing algorithm, which is used to construct the multiobjective optimization approach. Simulation and experimental results demonstrate that the proposed model accurately predicts the energy consumption of the FPTLIR across different paths and flight conditions with an average relative error ranging from 0.76% to 3.24%. After optimization, energy savings of 5.33% and 5.01% are achieved for on-line and off-line missions, respectively, while maintaining the shortest execution time at the given energy level. The energy consumption optimization approach significantly improves the flight efficiency of the system, providing a reference for analyzing and optimizing energy consumption of inspection robots.

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.

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

Neural Radiance Fields-Based 3D Reconstruction of Power Transmission Lines Using Progressive Motion Sequence Images

To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying–walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an enhanced method based on the Neural Radiance Fields (NeRF) method for reconstructing PTLs. The highlights of PL-NeRF include (1) compressing the unbounded scene of PTLs by exploiting the spatial compression of normal L∞; (2) encoding the direction and position of the sample points through Integrated Position Encoding (IPE) and Hash Encoding (HE), respectively. Compared to existing methods, the proposed method demonstrates good performance in 3D reconstruction, with fidelity indicators of PSNR = 29, SSIM = 0.871, and LPIPS = 0.087. Experimental results highlight that the combination of PL-NeRF with progressive motion sequence images ensures the integrity and continuity of PTLs, improving the efficiency and accuracy of image-based reconstructions. In the future, this method could be widely applied for efficient and accurate 3D reconstruction and inspection of PTLs, providing a strong foundation for automated monitoring of transmission corridors and digital power engineering.

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.

Design of an Over-Actuated Hexacopter Tilt-Rotor for Landing and Coupling in Power Transmission Lines

It is known that new power transmission line inspection techniques have been developed over the last few years with great potential to improve and, in some cases, even replace traditional inspection procedures such as using helicopters and cars. A series of Unmanned Aerial Vehicles (UAVs) such as fixed-wing or rotary-wing UAVs, and vehicles that climb on the power transmission line, promise to revolutionize the inspection market. In this light, at least 39 new research studies and/or products have been conducted and/or introduced to the market, respectively. However, in an incipient way, some works point to the fusion of some technologies: the development of multi-rotor UAVs and the ability to connect and move over the power transmission line. In line with this, the current work was proposed, with significant unprecedented advances (such as an over-actuated control capacity with tilt rotors, the capability of a displacement in the angle, and the maintenance of active motors on the power transmission line), and the design, modeling, and control of an over-actuated UAV able to move over the conductor cable without the need for a new locomotion system is presented. The aircraft allows for a greater response and the indispensable ability to approximate landing in a power transmission line arbitrary position rather than the catenary lowest point (due to its ability to forward/backward move using the tilting rotors). Its design is detailed, its subsystems are described, and its normal and coupled flight mode dynamics are modeled. The results show good stability and reliable maneuvers for the coupling-to-power-transmission-line flight mode, without any overshoots, and the ability to follow the entire catenary through different Real Control Action (RCA) sets.

Dynamic performance analysis based on the mechatronic system of power transmission line inspection robot with dual-arm

Dynamic performance of the power transmission line inspection robots (PTLIRs) is of great importance, which would heavily affect the stability of the robot during obstacle avoidance. In this paper, a detailed dynamic performance analysis method of PTLIR is presented by considering the effect on the mechatronic system caused by obstacle avoidance. First, the dynamic modeling of the robot and control system model of the revolute joint are given based on the mechatronic modeling. Then, dynamic characteristics of mechanical subsystem are analyzed by studying the change of the inertia matrix during obstacle avoidance. Next, to analyze the control subsystem performance, three kinds of pole assignment methods which used to tune the controller parameter are compared. Finally, a series of numerical simulations are performed, which prove the time-varying characteristic of load inertia during obstacle avoidance has a great influence on the dynamic performance of the robot, moreover, the changes of natural angular frequency and damping coefficient parameters could be used to evaluate dynamic performance. The main results of the paper can provide an effective tool to analysis dynamic performance of the PTLIR, which is meaningful to improve inspection stability.

Path Planning of Unmanned Aerial Systems for Visual Inspection of Power Transmission Lines and Towers

The inspection of power transmission and distribution systems is performed visually by foot patrolling and helicopter methods. These methods have various disadvantages such as time-consuming, high operating cost, safety issues, and improper results. To overcome this issue, power utility companies are searching for the alternatives like Unmanned Aerial Systems (UAS) or drones. UAS are safe, cost effective, and requires less time for power transmission line inspection compared to the regular methods. In this manuscript, a decisive flight path planning for UAS to visually inspect a power transmission line and towers is explained. The objective of this paper is to maximize the performance of three functions such as coverage of transmission tower, quality of captured image, and flight time. Second, proposing an automated inspection strategy for UAS to follow the overhead power transmission lines. These objectives are achieved by formulating a cost function, to convert the path planning into a safe operation for UAS. The results of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are compared to find the best path for UAS. The experimental finding shows that PSO algorithm has higher efficiency and effectiveness compared to the SA algorithm and benefits the UAS with a safe flight path for the inspection of power transmission towers.

Design and Application of a UAV Autonomous Inspection System for High-Voltage Power Transmission Lines

As the scale of the power grid continues to expand, the human-based inspection method struggles to meet the needs of efficient grid operation and maintenance. Currently, the existing UAV inspection system in the market generally has short endurance power time, high flight operation requirements, low degree of autonomous flight, low accuracy of intelligent identification, slow generation of inspection reports, and other problems. In view of these shortcomings, this paper designs an intelligent inspection system based on self-developed UAVs, including autonomous planning of inspection paths, sliding film control algorithms, mobile inspection schemes and intelligent fault diagnosis. In the first stage, basic data such as latitude, longitude, altitude, and the length of the cross-arms are obtained from the cloud database of the power grid, while the lateral displacement and vertical displacement during the inspection drone operation are calculated, and the inspection flight path is generated independently according to the inspection type. In the second stage, in order to make the UAV’s flight more stable, the reference-model-based sliding mode control algorithm is introduced to improve the control performance. Meanwhile, during flight, the intelligent UAV uploads the captured photos to the cloud in real time. In the third stage, a mobile inspection program is designed in order to improve the inspection efficiency. The transfer of equipment is realized in the process of UAV inspection. Finally, to improve the detection accuracy, a high-precision object detector is designed based on the YOLOX network model, and the improved model increased the mAP0.5:0.95 metric by 2.22 percentage points compared to the original YOLOX_m for bird’s nest detection. After a large number of flight verifications, the inspection system designed in this paper greatly improves the efficiency of power inspection, shortens the inspection cycle, reduces the investment cost of inspection manpower and material resources, and successfully fuses the object detection algorithm in the field of high-voltage power transmission lines inspection.

LECast: A Low-Energy-Consumption Broadcast Protocol for UAV Blockchain Networks

With the continuous development of communication technology, drones are playing an important role in many fields, such as power transmission line inspection and agricultural pesticide spraying. In order to protect the data privacy and communication security of drones, many experts are considering blockchain as its enabling technology. However, due to their small size and limited power storage, drones cannot support energy-intensive blockchain applications. In addition, the future 6G communications need to implement an important key performance indicator, namely extremely low-power communications (ELPCs). As a consequence, research into green blockchain is becoming more and more popular. The broadcast of the blockchain is one of the most energy-intensive parts because it entails flooding and there are a lot of unnecessary communication processes. Therefore, in order to make blockchain more suitable for ELPC requirements in 6G communications and unmanned aerial vehicle (UAV) networks, we took the blockchain broadcast as an improvement candidate and designed LECast, a low-energy-consumption protocol. LECast first analyzes the energy consumption model of the communication between two drones and constructs the shortest-path broadcast tree (SPB Tree) for the UAV networks to minimize energy consumption. Meanwhile, to make the sending drone address the receiving drone in a more convenient way, we proposed an extended Huffman coding (EHC) scheme to name the drones. Furthermore, the other issues with the broadcast tree are reliability and security. When a channel fails, subsequent drones cannot smoothly receive the transaction or block data. As a result, we introduced multichannel transmission with splitting data (MTSD); that is, the transaction or block data are divided into segments and transmitted in parallel multiple times over multiple channels. Finally, through the analysis and simulation of LECast in terms of energy consumption, latency, throughput, reliability, security, and coverage rate, the advantages of LECast were confirmed, which could meet the requirements of ELPCs and be well applied to UAV networks.

Improved YOLOv5s model for key components detection of power transmission lines.

High-voltage transmission lines are located far from the road, resulting in inconvenient inspection work and rising maintenance costs. Intelligent inspection of power transmission lines has become increasingly important. However, subsequent intelligent inspection relies on accurately detecting various key components. Due to the low detection accuracy of key components in transmission line image inspection, this paper proposed an improved object detection model based on the YOLOv5s (You Only Look Once Version 5 Small) model to improve the detection accuracy of key components of transmission lines. According to the characteristics of the power grid inspection image, we first modify the distance measurement in the k-means clustering to improve the anchor matching of the YOLOv5s model. Then, we add the convolutional block attention module (CBAM) attention mechanism to the backbone network to improve accuracy. Finally, we apply the focal loss function to reduce the impact of class imbalance. Our improved method's mAP (mean average precision) reached 98.1%, the precision reached 97.5%, the recall reached 94.4% and the detection rate reached 84.8 FPS (frames per second). The experimental results show that our improved model improves the detection accuracy and has advantages over other models in performance.

Application of UAV in surveying objects of high-voltage line and power transmission lines and removing damages

The use of unmanned aerial vehicles (UAV) in the inspection of power transmission lines (TL) objects and elimination of damages is considered. The problem urgency of the operational search for damages with their subsequent elimination at high-voltage lines (HVL) and power transmission lines is substantiated. The factors leading to the disruption of the equipment and facilities of power transmission lines objects are determined. It was revealed that in modern conditions the use of UAV allows not only monitoring the objects of HVL and power transmission lines, but also certain types of work. The experience of using UAVs for searching and analyzing damage of HVL and power transmission line objects in Russia and other countries is considered. It is concluded that the advantages of using UAVs in the energy sector include UAV maneuverability, solving issues of mapping and aerial photography, modern technical support for the safety of power grids, the ability to receive information in a short time and in real time, automation of certain types of work, reducing the number of field trips, reducing scope of work in the high voltage zone and at high altitudes, which reduces the risks associated with the human factor. Keywords unmanned aerial vehicle (UAV), high-voltage line (VL), power transmission line (PTL), thermal imaging survey, aerial monitoring, operation of facilities and equipment

A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset

To address power transmission lines (PTLs) traveling through complex environments leading to misdetections and omissions in fitting recognition using cameras, we propose a fitting recognition approach combining depth-attention YOLOv5 and prior synthetic dataset to improve the validity of fitting recognition. First, datasets with inspection features are automatically synthesized based on prior series data, achieving better results with a smaller data volume for the deep learning model and reducing the cost of obtaining fitting datasets. Next, a unique data collection mode is proposed using a developed flying-walking power transmission line inspection robot (FPTLIR) as the acquisition platform. The obtained image data in this collection mode has obvious time-space, stability, and depth difference, fusing the two data types in the deep learning model to improve the accuracy. Finally, a depth-attention mechanism is proposed to change the attention on the images with depth information, reducing the probability of model misdetection and omission. Test field experiments results show that compared with YOLOv5, the mAP5095 (mean average precision on step size 0.05 for thresholds from 0.5 to 0.95) of our depth-attention YOLOv5 model for fitting is 68.1%, the recall is 98.3%, and the precision is 98.3%. Among them, AP, recall, and precision increased by 5.2%, 4.8%, and 4.1%, respectively. Test field experiments verify the feasibility of the depth-attention YOLOv5. Line field experiments results show that the mAP5095 of our depth-attention YOLOv5 model for fittings is 64.6%, and the mAPs of each class are improved compared with other attention mechanisms. The inference speed of depth-attention YOLOv5 is 3 ms slower than the standard YOLOv5 model and 10 ms to 15 ms faster than other attention mechanisms, verifying the validity of the depth-attention YOLOv5. The proposed approach improves the accuracy of the fitting recognition on PTLs, providing a recognition and localization basis for the automation and intelligence of inspection robots.

DCPLD-Net: A diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-Borne LiDAR data

The stable and reliable supply of electric power is strongly related to the normal social production. In recent years, power transmission lines inspection based on remote sensing methods has made great progress, especially using the UAV-borne LiDAR system. The extraction and identification of power transmission lines (i.e., conductors) from 3D point clouds are the basis of LiDAR data-based power grid risk management. However, existing rule-based/traditional machine learning extraction approaches have exposed some limitations, such as the lack of timeliness and generalization. Moreover, the potential of deep learning is seriously overlooked in RoW (Right of Way) LiDAR inspection tasks. Thus, we proposed DCPLD-Net: a diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-borne LiDAR data. To implement efficient 2D convolution on 3D point clouds, we proposed a novel point cloud representation, named Cross Section View (CSV), which transforms the discrete point clouds into 3D tensors constructed by voxels with a deformed geometric shape along the flight trajectory. After the CSV feature generation, the encoded features of each voxel are treated as energy (i.e., heat) signals and diffused in space to generate diffusion feature maps. The feature of the power line points are thus enhanced through this simulated physical process (diffusion). Finally, a single-stage detector named PLDNet is proposed for the multiscale detection of conductors on the diffused CSV representations. The experimental results show that the DCPLD-Net achieves an average F1 score of 97.14 % at 8 Hz detection frequency on RoWs inspection LiDAR datasets collected by both mini-UAV LiDAR and large-scale fully autonomous UAV power lines inspection robots, and surpasses compared methods (i.e. PointNet ++, RandLA-Net) in terms of F1 scores and IoU.

Class-aware edge-assisted lightweight semantic segmentation network for power transmission line inspection

Class-aware edge-assisted lightweight semantic segmentation network for power transmission line inspection

Key target and defect detection of high-voltage power transmission lines with deep learning

Automatic visual detection of key targets and defects for power transmission lines based on power transmission line inspection robots (PTLIR) and unmanned aerial vehicles (UAVs) is an ongoing trend in the smart grid development. The advancement of deep learning has accelerated the intelligence of power grid inspection. In terms of the power transmission line fittings detection application, improving the detection accuracy of small targets and defects using a deep learning detection network is challenging, owing to the complex background lighting characteristics of high-voltage power transmission lines in the wild. To address this problem, we present an inspection method for key targets and defects in high-voltage power transmission lines based on a deep learning object detection network. First, we collected sample images of key targets under different backgrounds, lighting conditions, and postures. Further, data augmentation was performed to solve the problem of imbalance in the number of target categories, and a large standard dataset was constructed. Second, we improved the extraction ability of small object features by optimizing the detection network. The precision and recall rate of the optimized detection network were 93.5% and 96.2%, respectively. Furthermore, small targets and defects in a complex environment could be successfully detected. Additionally, the detection of targets and defects in the inspection videos recorded by the PTLIR and UAVs were realized. Experimental results demonstrated that the proposed method performed well in the detection accuracy of key targets and defects in similar high-voltage power transmission line environments. It can realize remote, automatic inspection of high-voltage power transmission lines in the field.

Precise Motion Control of a Power Line Inspection Robot Using Hybrid Time Delay and State Feedback Control.

Intelligent robotic inspection of power transmission lines has proved to be an excellent alternative to the traditional manual inspection methods, which are often tedious, expensive, and dangerous. However, to achieve effective automation of the robots under different working environments, the dynamic analysis and control of the robots need to be investigated for an efficient inspection process. Nonetheless, the application of control techniques for the position, speed and vibration control of these robots has not been explored in detail by the existing literature. Thus, an approach for precise motion control of the sliding inspection robot is presented in this paper. The main contribution of the study is that the chattering problem associated with the traditional command shaping time delay control (TDC) was minimized by smoothing the chattered input signal. Then, the improved control (iTDC) which is effective for oscillation control is hybridized with a pole placement based feedback control (PPC) to achieve both position and the sway angle control of the robot. The nonlinear and the linearized models of the sliding robot were established for the control design and analysis. Three parameters of the robot, namely, the length of the suspended arm, the mass of the payload, and the friction coefficient of different surfaces, were used to assess the robustness of the controller to model uncertainties. The iTDC + PPC has improved the velocity of TDC by 201% and minimizes the angular oscillation of PPC by 209%. Thus, the results demonstrate that the hybridized iTDC + PPC approach could be effectively applied for precise motion control of the sliding inspection robot.

Parameters Optimization of UAV for Insulator Inspection on Power Transmission Line

In recent years, Unmanned Aerial Vehicle (UAV) technology has been widely used in power transmission line inspection. UAV operating parameters determine the quality of the acquired insulator image, which directly affects the efficiency of transmission line inspection. Regarding the inspection of ceramic insulators, this work establishes a three-dimensional (3D) model of ceramic insulators, and simulates the changes in shooting distance, angle, and height of the UAV through three geometric transformations: rotation, translation, and scaling. The influence of those three UAV operating parameters (the shooting distance, angle, and height) on the integrity of the ce-ramic insulator steel cap is studied. In the infrared zero value detection of 110kV ceramic insulators, It is found that the best setting for obtaining the maximum steel cap area is: shooting distance of 3 - 7 meters, shooting height of 1 meter below the center of the insulator string, and a shooting depression angle of 0°. The UAV operating parameters setting method proposed in this paper will provide theoretical and practical basis for the in-depth intelligent inspection of transmission line insulators.