Power Transmission Line Inspection Research Articles

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.

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.

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.

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.

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.

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.

A novel unit mechanism for serial head-tail alternatively supported robot

Multi-unit serial mobile robot designed for obstacle avoidance, inspection or rescue task, should ideally be able to switch the head and tail as supporting stand to move forward wriggly, be lightweight and low energy consumption, aiming at adapting to complex application environment. This paper proposes a novel robot pitching unit based on a parallelogram mechanism, and a robot with the proposed units taking the task of power transmission line inspection as application background. To reduce unit motor number and weight, a single motor and cable driving(SMCD) method is proposed. The unit architecture and robot prototype are designed. The kinematic model is developed and the supporting stand switch process is presented. According to the force analysis of the unit mechanisms, the driving motor energy consumption calculating methods of the proposed unit and the conventional serial unit are presented respectively. The low energy consumption characteristic of the proposed unit is demonstrated through applications respectively on a 4-unit serial immobile robot model and an inspection robot model for power transmission line. Experiments of unit supporting stand switch and robot prototype crossing obstacles are conducted to verify the unit feasibility. The results of analysis and experiments show that comparing with the conventional units, the whole energy consumption of the robot can be reduced clearly by applying the proposed units, the novel unit is capable of switching the supporting stand, based on which the inspection robot meets the application requirements on overhead power transmission line.

Solving the length constrained [formula omitted]-drones rural postman problem

In this paper we address the Length Constrained K-Drones Rural Postman Problem (LC K-DRPP). This is a continuous optimization problem where a fleet of homogeneous drones have to jointly service (traverse) a set of (curved or straight) lines of a network. Unlike the vehicles in classical arc routing problems, a drone can enter a line through any of its points, service a portion of that line, exit through another of its points, then travel directly to any point on another line, and so on. Moreover, since the range of the drones is restricted, the length of each route is limited by a maximum distance. Some applications for drone arc routing problems include inspection of pipelines, railway or power transmission lines and traffic monitoring.To deal with this problem, LC K-DRPP instances are digitized by approximating each line by a polygonal chain with a finite number of points and allowing drones to enter and exit each line only at these points. In this way we obtain an instance of the Length Constrained K-vehicles Rural Postman Problem (LC K-RPP). If the number of points used to discretize the lines is large, the LC K-RPP instance can be extremely large and, hence, very difficult to solve optimally. Even heuristic algorithms can fail in providing feasible solutions in reasonable computing times. An alternative is to generate smaller LC K-RPP instances by approximating each line with few but “significant” segments.We present a formulation and some valid inequalities for the LC K-RPP. Based on this, we have designed and implemented a branch-and-cut algorithm for its solution. Moreover, in order to be capable of providing good solutions for large LC K-RPP instances, we propose a matheuristic algorithm that begins by finding good solutions for the LC K-RPP instance obtained by approximating each line by a single segment. Then, to find better solutions, some promising intermediate points are sequentially incorporated. Extensive computational experiments to assess the performance of both algorithms are performed on several sets of instances from the literature.

Dynamic Modeling and DME Evaluation of Power Transmission Line Inspection Robots

Dynamic Modeling and DME Evaluation of Power Transmission Line Inspection Robots

A mobility compensation method for drones in SG-eIoT

In order to achieve the specific goal of a smart grid, the concept of electricity Internet of Things (eIoT) has been proposed to assist the monitoring and inspection of power transmission line state and optimize the asset utilization. The long power transmission line and the complex field operation environment urge the introduction of drones into the eIoT for fast power transmission line inspection, data collection from sensors for further big data analysis, adaptive control of power line voltage, etc. Additionally, drones can also act as a central communication control or relay point to serve the data exchange among sensors, drones and power transmission line maintenance personnel in the scenario where the conventional mobile communication service is not available. However, the fast mobility of drones may affect the signal transmission and position estimation performance, which may further deteriorate the networking performance. In order to solve this problem, a mobility compensation method is proposed, which includes the steps of frequency offset estimation and relative velocity calculation. Through the Monte Carlo simulations, the proposed algorithm shows favorable gains compared with the conventional ones.

A Novel Foreign Object Detection Algorithm Based on GMM and K-Means for Power Transmission Line Inspection

The detection of foreign objects on transmission lines is an important research content of intelligent inspection in smart grid. The foreign objects on the transmission line tower will cause adverse effects on the transmission line and other equipment, and even endanger the safe operation of the power grid. In order to accurately identify foreign objects on power transmission lines, this paper proposes an unsupervised foreign object detection algorithm based on GMM (Gaussian Mixture Model) and k-means. Firstly, K-means is used for clustering, and then GMM is used for clustering. Finally, the foreign objects on the power transmission line are identified according to the clustering results. Experimental results show that the proposed algorithm has a high recognition rate. In addition, the more samples, the higher the recognition accuracy.

Transmission Line Wire Following System Based on Unmanned Aerial Vehicle 3D Lidar

UAV(Unmanned aerial vehicle) wire checking is an efficient and scientific solution of transmission line operations. Aiming at the existing technology, this paper put forward a kind of based on 3D laser radar and ROS (Robot Operating System) of the UAV wire follow System. In the process of power transmission line inspection of wire, the system capture the wire by 3D laser radar mounted on the UAV and calculate the space distance between UAV and the wire and angle of this distance in 3D laser radar coordinate axis, control drone aircraft by PID(Proportion Integration Differentiation) algorithm in the attitude of UAV and inspection speed by the ROS on the onboard computer system platform for real-time wire following and automatic inspection task. Field experiments show that the system can effectively improve the efficiency and safety of wire inspection, and is conducive to the automation, safety and informatization of transmission line inspection process.

Technology revolution in the inspection of power transmission lines – A literature review

Technology revolution in the inspection of power transmission lines – A literature review

Multi-Scale Feature Enhanced Domain Adaptive Object Detection For Power Transmission Line Inspection

Domain adaptive object detection aims to build an object detector for the unlabeled target domain by transferring knowledge from a well-labeled source domain, which can alleviate the problem of cumbersome labeling of object detection in cross-scene power transmission line inspection. Remarkable advances are made recently by mitigating distributional shifts via hierarchical domain feature alignment training of detection networks. However, domain adaptive object detection is still limited in learning the invariance representation of multi-scale features. Specifically, the scale of objects varies in the scenes of aerial inspection, which hinders the knowledge transfer from the labeled source domain. In this paper, we propose a multi-scale feature enhanced domain adaptation method for cross-domain object detection of power transmission lines inspection. The proposed method consists of two components: 1) Multi-Scale Fusion Feature Alignment module (MSFA) to strengthen similar representation characteristics of different scales object in domain adaptive by utilizing context information conveyed from other levels; 2) Multi-Scale Consistency Regularization module (MSCR) to jointly optimize the multi-scale feature learning of each level, which promotes domain invariant feature learning at each level. Experimental results demonstrate that our method significantly increases the performance of the object detector in several cross-scene transmission line inspection tasks.

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

Image online monitoring technology has been widely used in transmission lines inspection, but the intelligent and efficient foreign object detection still has a gap with the ideal. In this paper, we propose a deep learning method to detect invading foreign objects for power transmission line inspection. Specifically, we design our network based on the regression strategy with oriented bounding boxes to accurately predict spatial location and orientation angle of foreign objects, as well as their categories in cluttered backgrounds. Moreover, an easy yet effective Scale Histogram Matching method is proposed to be applied to the publicly available dataset, allowing useful patterns to be exploited to detect tiny foreign objects during the pretraining procedure and boosting detection performance even with limited annotated samples. Besides, we construct an image dataset that contains common foreign objects in transmission line scenarios to evaluate proposed methods, on which experiment results show our full model achieves accuracy with 88.1% mean Average Precision (mAP). Additionally, the efficient and compact network structure allows our network to run in real-time, which provides possibilities for practical use.