Transmission Line Inspection Research Articles

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

Analisis Penentuan Lokasi Gangguan Hubung Singkat Pada Saluran Transmisi Di Sulawesi Tenggara

The electricity system often experiences disturbances, especially short circuit disturbances on transmission lines. This short circuit disturbance is caused by various things such as lightning surges, fallen trees, animals, and other conditions. The interconnected Southeast Sulawesi power system cannot be separated from this short circuit problem. Therefore, determining the location of this short-circuit fault must be done quickly so that the cause of the disturbance can be immediately identified. This study aims to analyze the determination of the location of short circuit faults using the Takagi Method. Simulations were carried out using ETAP 16.0.0 software to see the condition of the Southeast Sulawesi system power flow. This Takagi method analyzes the location of the disturbance using the conditions before and after the disturbance occurs based on the recorded data at the substation. The results of the analysis carried out on the Southeast Sulawesi transmission line show that the percentage of accuracy in determining the location of a single-phase short-circuit fault to ground using the Takagi method is better, with a distance difference of 0.368-0.581 km (1.49%) when compared to the reading results on recordings of 10,173-9,960 km (31.58%) from the fault location occurred after inspection of the Lasusua-Kolaka transmission line. Disturbances on lines 1 and 2 Kolaka-Lasusua produce an error value of 1.533 km (9.24%) and 1.547 km (9.32%) when compared to fault locator readings of 3.212 km (19.36%) and 1.812 km ( 10.92%). Meanwhile, the Unaaha-Kolaka transmission line produces a distance difference of 0.709 km (3.29%) when compared to the fault locator reading of 4.323 km (20.12%). The results of the power flow simulation produce an installed generating capacity of 188 MW, a total active power of 116.780 MW and a reactive power of 29,944 Mvar. Thus, it can be concluded that the Takagi Method has a better accuracy value in reading fault locators.

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.

A survey of intelligent transmission line inspection based on unmanned aerial vehicle

A survey of intelligent transmission line inspection based on unmanned aerial vehicle

UAV system with trinocular vision for external obstacle detection of transmission lines.

External obstacle detection is a significant task in transmission line inspection and is related to the safe operation of the power transmission grid. In recent years, unmanned aerial vehicles (UAVs) equipped with different devices have been widely used for transmission line inspection. However, because of the complex environment of transmission lines and weak power line textures in the obtained images, most existing methods and systems cannot meet the requirements for real-time and high-accuracy external obstacle detection of transmission lines. In this paper, a novel, to the best of our knowledge, UAV system integrated trinocular vision technology with remote sensing is developed to achieve better external obstacle detection of transmission lines in real time, which is composed of a DJ-Innovations (DJI) UAV equipped with a global positioning system (GPS), angle sensors, trinocular vision including three visible cameras with the same parameters, and a small processor with a pre-implanted software algorithm. In this paper, a new method for external obstacle detection of transmission lines is proposed to satisfy the requirements for real-time and high-accuracy practical inspection applications. First, the original trinocular images need to be rectified. Then, the rectified trinocular images are adopted to achieve three-dimensional reconstruction of power lines. Finally, based on trinocular vision, bag of feature, and GPS, the clearance distance measurement, obstacle classification, and obstacle location are realized. Experimental tests on 220 kV transmission lines reveal that our proposed system can be applied in practical inspection environments and has good performance.

Exfoliated and bulk β-gallium oxide electronic and photonic devices

<h2>Abstract</h2> Monoclinic gallium oxide (β-Ga₂O₃) has recently attracted increasing attention due to the availability of high quality, large diameter single crystals from mature melt growth methods and its attractive material properties of wide E_G of 4.6–4.9 eV, good electron mobility (>200 cm²/Vs) and estimated E_c of ~8 MV/cm. Compared to other ultra-wide bandgap materials, the inexpensive substrates lower the manufacturing cost for β-Ga₂O₃ power devices. Applications of deep-UV detectors with solar blindness include inspection of high voltage transmission lines for corona and partial discharges, detection of fires and various defense systems. Si- and GaAs-based UV PDs are not truly solar-blind, as additional, bulky visible-light blocking filters are required. In this short review we discuss the use of bulk and exfoliated β-Ga₂O₃ for these applications and relate this to the basic surface and response of the material to high temperatures, radiation exposure and typical semiconductor processing steps. The material can easily be cleaved along the (100) surface and layers with (100)A surface termination have higher energy than the (100)B termination. The (100)B cleavage plane is the favorable one, observed experimentally.

Transmission line inspection image detection based on improved Faster-RCNN

Transmission line inspection image detection based on improved Faster-RCNN

Pointer meter recognition in UAV inspection of overhead transmission lines

At present, UAV (unmanned aerial vehicle) inspection has been widely used in the operation and maintenance of overhead transmission lines. However, the instrument indications of overhead lines are reading usually by climbing artificially or manual reading from UAV videos. Compared with pointer meter recognition applications in other fields, the pointer meter recognition in UAV inspection is more difficult,due to its various complex photo conditions. In this paper, the intelligent recognition system of pointer arrester counter in UAV inspection of transmission lines is studied, a pointer meter recognition method based on deep learning is proposed, which can effectively identify meter indications under various brightness levels and camera angles in inspection videos.

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.

Design of Quadrotor System for Transmission Line Inspection

A novel robust adaptive control is proposed for target detection and recognition in transmission line inspection, this paper proposes a quadrotor inspection system based on robust adaptive control of multi-sensor fusion. Robust adaptive control ensures the stability and anti-disturbance ability of the quadrotor during the flight. It is measurable for the distance between the quadrotor and cables by laser sensor and ultrasonic sensor. It can be used to detect and identify the target objects on cable lines by OpenMV. According to the proposed Lyapunov function, the asymptotically stable conditions is obtained, in which all trajectories converge in finite time to the desired location. Finally, the effectiveness of inspections is validated experimentally, and the system has strong anti-interference ability.

Automatic Obstacle-Crossing Planning for a Transmission Line Inspection Robot Based on Multisensor Fusion

Accurate obstacle detection and proper behavior planning are key factors in the success of transmission line inspection robots. To achieve the autonomous location and identification of line obstacles in the operation of transmission line inspection robots, we propose a method in which information from various sensors is used to control a robot such that it can reliably and stably approach, locate, and identify obstacles. The accuracy and real-time requirements of obstacle information in the autonomous operation of the inspection robot are analyzed, a multi-sensor integrated structure for line obstacle location and identification is proposed, and an obstacle location and identification algorithm is designed for each stage. The principle of monocular vision ranging is used to control the robot such that it approaches obstacles and enters the short-range location stage, where the reliable location of obstacles is achieved via collision-encoder-current sensor data fusion. Obstacles are identified and the identification results are combined with those of the approaching rolling phase. The robot state vector is constructed according to the obstacle detection information and information measured by the robot sensors. Based on the current state vector, combined with the robot obstacle surmounting process, an obstacle surmounting behavior planning method based on multi-sensor fusion is implemented. Experiments were conducted using laboratory-simulated lines to verify the effectiveness of the proposed method for obstacle detection and behavior planning during transmission line inspection.

Power Line-Guided Automatic Electric Transmission Line Inspection System

The existing unmanned aerial vehicle (UAV)-based electric transmission line inspection systems generally adopt manual control and follow the predefined path, which reduces the efficiency and makes a high inspection cost. In this article, a UAV system with advanced embedded processors and binocular visual sensors is developed to generate guidance information from power lines in real-time and achieve automatic transmission line inspection. To realize 3-D autonomous perception of power lines, we first propose an end-to-end convolutional neural network (CNN) to extract complementary information from multilevel features and detect power lines with different pixel widths and orientations. Specifically, multilevel feature aggregation module fuses multilevel features within the same stage by learning the weight vector related to the content. The joint attention (JA) module is proposed to extract rich semantic information and suppress the background noises. Meanwhile, multistage detection results are fused to enhance the robustness of the proposed network. Subsequently, power lines are grouped according to the morphological characteristics of thinning detection results, and 3-D point sets of power lines are constructed based on the epipolar constraint of binocular images. Finally, the target point of current stereo images is generated by projecting 3-D power line points to the horizontal and vertical planes. The few-waypoint trajectory is generated based on continuous target points, and automatic inspection is finished with the proposed real-time motion planning strategy. Experimental results on four datasets show that the proposed power line detection method outperforms other state-of-the-art methods. The developed UAV platform and the proposed autonomous inspection strategy are evaluated in practical environments to validate the robustness and effectiveness.

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&#x00B0;. 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.

Research and Application of Laser Doppler Vibration Measurement on Mechanical Defect Inspection of 1100kV Gas Insulated Transmission Line

Research and Application of Laser Doppler Vibration Measurement on Mechanical Defect Inspection of 1100kV Gas Insulated Transmission Line

Fault Diagnosis of Transmission Lines Based on High Frequency Electromagnetic Spectrum Distribution Characteristics

According to the recognition of the fault of the transmission line, the traditional methods of manual detection, image recognition and infrared observation can not distinguish the defects, but the line produces the electromagnetic interference level change is its produces the flaw the important characteristic. Therefore, the electromagnetic interference (EMI) can be used as a parameter in the inspection of high-voltage transmission line. this paper studies the correlation between the fault of the transmission line and the distribution of the high frequency electromagnetic spectrum, by analyzing the level change of the electromagnetic interference between the normal wire and the broken wire, the defects such as the old wire, the broken wire and the serious corrosion, which can not be distinguished by image, are detected, it provides a strong technical support for the condition assessment of transmission line conductors.

Design and Application of Single-Arm Live Working Robot System on Transmission Line

The power transmission line live working robot walks on the power transmission line through wheels. The robot arm and its end tools carried by the robot body can assist or even replace the manual implementation of live inspection and operation of the power transmission line. It has important theoretical significance and practical application value for improving work efficiency and solving dangerous problems in the operation process. In this paper, a single-arm live working robot is designed and developed for a variety of maintenance tasks on transmission lines. At the same time, a method of autonomous end reconfiguration on transmission line is proposed, so as to automatically realize transmission line inspections and live operation. The robot physical model and the motion planning of the robot manipulator arm for the typical fittings maintenance operation are given. The physical prototype system of the live working robot is developed through the system integration design. Finally, related live working experiments were carried out on the simulation test line so as to verify the feasibility and effectiveness of the single-arm live working robot realize the inspection operation of the power transmission line. The research in this article has important theoretical significance and practical application value for intelligent operation and maintenance management of transmission grids.

Insulator Semantic Segmentation in Aerial Images Based on Multiscale Feature Fusion

As one of the important components in the transmission line, the insulator is related to the safe and reliable operation of the entire transmission line. Aerial images are characterized by complex backgrounds, multiple pseudotargets, and low signal‐to‐noise ratios. Rapid and accurate localization of insulators in aerial images is a critical and challenging task in automatic inspection of transmission lines. Most insulator localization methods suffer from the loss of target edge detail information and large amount of model parameters. To solve these problems, this paper adopts an Encoder‐Decoder architecture, called ED‐Net, to realize end‐to‐end intelligent and accurate identification of insulators in aerial images. Firstly, Initial Module and CA‐Bottleneck which are used to extract features from images to generate finer feature maps are proposed in the Encoder path. Meanwhile, global average pooling is used to preserve the maximum receptive field. Secondly, in the Decoder path, Refinement Boundary Module and Asymmetric Convolution Module are given to perform boundary optimization on the feature map, which are generated by the Encoder path. Finally, the Attention Feature Fusion Module is introduced into the Decoder path to combine high‐level features with low‐level features better and reduce the gap between features of different levels. The proposed model architecture keeps a suitable balance between the model parameters and insulator segmentation performance on insulator test datasets. Specifically, for a 512 × 512 input image, 95.12% mean intersection over union is achieved on the insulator test datasets with different environments and model parameters size being only 13.61 M. Compared with the current state‐of‐the‐art semantic segmentation methods, the results show that the proposed method has higher efficient and accuracy.

A New Multilabel Recognition Framework for Transmission Lines Bolt Defects Based on the Combination of Semantic Knowledge and Structural Knowledge

Automatic inspection of transmission lines has received extensive attention in recent years. Bolts are widely used as fasteners in transmission lines, and their defect images have the characteristics of large intra-class differences and small inter-class differences. In view of this, we proposed a multilabel image recognition framework for bolt defects (RFBD). The framework consists of a visual feature and semantic knowledge network (VFSKnet) and a visual feature and position knowledge network (VFPKnet). To learn the relationship between bolt labels, the VFSKnet utilizes the relation between bolt defect labels as professional posterior knowledge to guide the model. To capture the structured fine-grained features, the VFPKnet extracts and utilizes the significant structural features of bolts. Finally, these two sub-networks, VFSKnet and VFPKnet, are combined into a framework after weighting, i.e. RFBD, for the final recognition. By using the proposed RFBD, the label-level accuracy reaches 93.91%, and the image-level accuracy reaches 83.29%. Experimental results well demonstrate the superiority of the proposed model for bolt defect recognition.

Characterization of Silicone Rubber/MgO Nanocomposites for Grippers in Transmission Line Inspection Robots

The effect of addition of nanoparticles to silicone rubber is studied in this paper in the context of soft robotic applications to transmission line maintenance. Silicone rubber and polylactic acid (PLA) are used in the fabrication of these robotic structures. Desirable characteristics for the materials making the structure are (i) Low surface charge and space charge accumulation and (ii) High tensile and tear strength. To meet these requirements, addition of nano MgO to silicone rubber is proposed and studies pertaining to surface potential variation, space charge variation and dielectric parameters are performed. It is observed that 3wt% MgO added silicone rubber shows better performance than pure silicone rubber. Further, silicone rubber/MgO nanocomposite sandwiched PLA shows least space charge at the electrodes and at the interfaces of the sandwich. Moreover, the mechanical properties are also enhanced via the addition of nano MgO. In particular, 3wt% MgO added silicone rubber shows maximum tensile and tear strength. A direct correlation between tear strength and Shore A hardness is also observed. In addition, substantial improvement in viscoelastic properties of silicone rubber is observed with the addition of nano MgO.

FINet: An Insulator Dataset and Detection Benchmark Based on Synthetic Fog and Improved YOLOv5

The inspection of insulators and their defects is of great significance for ensuring the safety and stability of power system. Small sample is one of the main issues of insulator defect detection based on neural network. In this research, we release a dataset for insulators and self-explosive defects detection, and provide a benchmark based on improved YOLOv5, named Foggy Insulator Network (FINet). In this work, a synthetic fog algorithm is implemented and optimized. An insulator dataset (SFID) with 13000 images is constructed and released. The YOLOv5 network is improved into SE-YOLOv5 by introducing the channel attention mechanism, and a robust detection model with 96.2% F1 score for insulators and their defects is trained from scratch, and served as benchmark. The synthetic fog algorithm proposed in this paper can be widely used for data augmentation of various datasets. The trained model can be applied in the field of transmission line inspection. The source codes, datasets and tutorials are available on GitHub.