High-voltage Transmission Line Inspection Research Articles

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.

Deep-Learning-Based Semantic Segmentation Approach for Point Clouds of Extra-High-Voltage Transmission Lines

The accurate semantic segmentation of point cloud data is the basis for their application in the inspection of extra high-voltage transmission lines (EHVTL). As deep learning evolves, point-wise-based deep neural networks have shown great potential for the semantic segmentation of EHVTL point clouds. However, EHVTL point cloud data are characterized by a large data volume and significant class imbalance. Therefore, the down-sampling method and point cloud feature extraction method used in current point-wise-based deep neural networks hardly meet the needs of computational accuracy and efficiency. In this paper, we proposed a two-step down-sampling method and a point cloud feature extraction method based on local feature aggregation of the point clouds after down-sampling in each layer of the model (LFAPAD). We then established a deep neural network named PowerLine-Net for the semantic segmentation of the EHVTL point clouds. Furthermore, in order to test and analyze the performance of PowerLine-Net, we constructed a point cloud dataset for the EHVTL scenes. Using this dataset and the Semantic3D dataset, we implemented network parameter testing, semantic segmentation, and an accuracy comparison of different networks based on PowerLine-Net. The results illustrate that the semantic segmentation model proposed in this paper has a high computational efficiency and accuracy in the semantic segmentation of EHVTL point clouds. Compared with conventional deep neural networks, including PointCNN, KPConv, SPG, PointNet++, and RandLA-Net, PowerLine-Net also achieves a higher accuracy in the semantic segmentation of EHVTL point clouds. Moreover, based on the results predicted by PowerLine-Net, the risk point detection for EHVTL point clouds has been achieved, which demonstrates the important value of this network in practical applications. In addition, as shown by the results of Semantic3D, PowerLine-Net also achieves a high segmentation accuracy, which proves its powerful capability and wide applicability in semantic segmentation for the point clouds of large-scale scenes.

Insulators’ Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models

Insulators identification and their missing defect detection are of paramount importance for the intelligent inspection of high-voltage transmission lines. As the backgrounds are complex, some insulators may be occluded, and the missing defect of the insulator is so small that it is not easily detected from aerial images with different backgrounds. To address the above issues, in this study, a cascaded You Only Look Once (YOLO) models are mainly explored to perform insulators and their defect detection in aerial images. Firstly, the datasets used for insulators location and missing defect detection are created. Secondly, a new model is proposed to locate the position of insulators, which is improved in the feature extraction network and multisacle prediction network based on previous YOLOv3-dense model. An improved YOLOv4-tiny model is used to conduct missing defect detection on the detected insulators. And then, the proposed YOLO models are trained and tested on the built datasets, respectively. Finally, the final models are cascaded for insulators identification and their missing defect detection. The average precision of missing defect detection can reach 98.4%, which is 5.2% higher than that of faster RCNN and 10.2% higher than that of SSD. The running time of the cascaded YOLO models for missing defect detection can reach 106 frames per second. Extensive experiments demonstrate that the proposed deep learning models achieve good performance in insulator identification and its missing defect detection from the inspection of high-voltage transmission lines.

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.

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

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

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.

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

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

Research on Recognition of Electricity Fittings in High Voltage Transmission Lines

On the basis of analyzing the structure of common power fittings in high-voltage transmission lines and their image features, combined with the DNN deep neural network in machine learning, we proposed a model suitable for high-voltage transmission line inspection robots to identify the types of electric power fittings on the transmission lines. And design a fast ROI generation method suitable for recognizing fittings on power transmission lines. Then we verify the feasibility and rationality of the fitting identification model.

Autonomous Approach for High Voltage Transmission Line Inspection and Fault Detection

Autonomous Approach for High Voltage Transmission Line Inspection and Fault Detection

A Method Based on Multi-Sensor Data Fusion for UAV Safety Distance Diagnosis

With the increasing application of unmanned aerial vehicles (UAVs) to the inspection of high-voltage overhead transmission lines, the study of the safety distance between drones and wires has received extensive attention. The determination of the safety distance between the UAV and the transmission line is of great significance to improve the reliability of the inspection operation and ensure the safe and stable operation of the power grid and inspection equipment. Since there is no quantitative data support for the safety distance of overhead transmission lines in UAV patrol, it is impossible to provide accurate navigation information for UAV safe obstacle avoidance. This paper proposes a mathematical model based on a multi-sensor data fusion algorithm. The safety distance of the line drone is diagnosed. In these tasks, firstly, the physical model of the UAV in the complex electromagnetic field is established to determine the influence law of the UAV on the electric field distortion and analyze the maximum electric and magnetic field strength that the UAV can withstand. Then, based on the main factors affecting the UAV such as the maximum wind speed, inspection speed, positioning error, and the size of the drone, the adaptive weighted fusion algorithm is used to perform first-level data fusion on the homogeneous sensor data. Then, based on the improved evidence, the theory performs secondary fusion on the combined heterogeneous sensor data. According to the final processing result and the type of proposition set, we diagnose the current safety status of the drone to achieve an adaptive adjustment of the safety distance threshold. Lastly, actual measurement data is used to verify the mathematical model. The experimental results show that the mathematical model can accurately identify the safety status of the drone and adaptively adjust the safety distance according to the diagnosis result and surrounding environment information.

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

A two-wheel load balance control strategy for an HVTL inspection robot based on second-order sliding-mode

PurposeOverhead high-voltage transmission line (HVTL) inspection robots are used to inspect the transmission lines and/or maintain the infrastructures of a power transmission grid. One of the most serious problems is that the load on the front wheel is much larger than that on the back one when the robot travels along a sloping earth wire. Thus, ongoing operation of the inspection robot mainly depends on the front wheel motor’s ability. This paper aims to extend continuous operation time of the HVTL inspection robots.Design/methodology/approachBy introducing a traction force model, the authors have established a dynamic model of the robot with slip. The total load is evenly distributed to both wheels. According to the traction force model, the desired wheel slip is calculated to achieve the goal of load balance. A wheel slip controller was designed based on second-order sliding-mode control methodology.FindingsThis controller accomplishes the control objective, such that the actual wheel slip tracks the desired wheel slip. A simulation and experiment verify the feasibility of the load balance control system. These results indicate that the loads on both wheels are generally equal.Originality/valueBy balancing the loads on both wheels, the inspection robot can travel along the earth wire longer, improving its efficiency.

Robot for High Voltage Transmission Line Inspection

Robot for High Voltage Transmission Line Inspection

Method of Slipping Identification and Control for the Driving Wheel of a High Voltage Transmission Line Inspection Robot

When the inspection robot runs on the overhead transmission line, self-adaptive climbing ability is an important index to estimate the robot application to real complex conditions, especially in the mountainous areas. The slipping of driving wheel usually happens when the robot climbs the lines with slopes, which would increase the abrasion of the driving wheel, reduce the efficiency of operation and waste the energy. So it is important to develop the wheels slipping identification and control method to improve the climbing ability of the inspection robot. This paper proposed a self-adaptive slipping identification and control method aiming to solve the slipping problem of the driving wheels. Firstly, the mechanism and force situation of the robot has been analyzed when the wheel slipping happens. Then we discussed the major factors to control the robot slipping and developed a dynamic model to identify the wheel slipping. Finally, we proposed a slipping identification strategy and a fuzzy control-based slipping control method. This slipping identification and control method have been tested by the experimental and practical field experiments to make the robot run in the experimental lines and the high voltage transmission lines (HVTL) in real, respectively. The performance results indicate that the proposed method is effective to improve the robot self-adaptive capacity to climb on the lines with slope and enhance the autonomous climbing and running ability of the robot.

Research on Dynamic Characteristics of Serial-Parallel Combination Joint with Flexible-Cable

In order to design the humanoid dual-arms high voltage transmission line inspection robot, the robot model is given and the working mechanism of the robot crossing obstacles is introduced, and the motion trajectory of the robot when it is moving is analyzed. The dynamic equations of the serial driven and the serial-parallel driven robotic arm are presented based on Lagrange equation and the dynamic characteristics of the wrist and elbow joint are simulated. The numerical simulation result of the serial driven robotic arm shows that the elbow joint has great torque when the robot moves horizontally and vertically. However, the numerical simulation result of the serial-parallel driven robotic arm shows that the elbow joint torque reduces greatly comparing with the serial driven arm. By analyzing the results of the simulation experiment, it can be seen that this serial-parallel combination driven joint with flexible-cable can reduce the elbow torque greatly and improve the load capacity of robotic arm comparing with the serial driven joint, which can solve the problem that the torque of serial driven joint in dual-arms inspection robot is too high.