Power Pylon Research Articles

Power Pylon Type Identification and Characteristic Parameter Calculation from Airborne LiDAR Data

Power Pylon Type Identification and Characteristic Parameter Calculation from Airborne LiDAR Data

Power Pylon Reconstruction from Airborne LiDAR Data Based on Component Segmentation and Model Matching

In recent years, with the rapid growth of State Grid digitization, it has become necessary to perform three-dimensional (3D) reconstruction of power elements with high efficiency and precision to achieve full coverage when simulating important transmission lines. Limited by the performance of acquisition equipment and the environment, the actual scanned point cloud usually has problems such as noise interference and data loss, presenting a great challenge for 3D reconstruction. This study proposes a model-driven 3D reconstruction method based on Airborne LiDAR point cloud data. Firstly, power pylon redirection is realized based on the Principal Component Analysis (PCA) algorithm. Secondly, the vertical and horizontal distribution characteristics of the power pylon point cloud and the graphical characteristics of the overall two-dimensional (2D) orthographic projection are analyzed to determine segmentation positions and the key segmentation position of the power pylon. The 2D alpha shape algorithm is adopted to obtain the pylon body contour points, and then the pylon feature points are extracted and corrected. Based on feature points, the components of original pylon and model pylon are registered, and the distance between the original point cloud and the model point cloud is calculated at the same time. Finally, the model with the highest matching degree is regarded as the reconstructed model of the pylon. The main advantages of the proposed method include: (1) identifying the key segmentation position according to the graphical characteristics; (2) for some pylons with much missing data, the complete model can be accurately reconstructed. The average RMSE (Root-Mean-Square Error) of all power pylon components in this study was 15.4 cm. The experimental results reveal that the effects of power pylon structure segmentation and reconstruction are satisfactory, which provides method and model support for digital management and security analysis of transmission lines.

Cooperative UAV-UGV Autonomous Power Pylon Inspection: An Investigation of Cooperative Outdoor Vehicle Positioning Architecture.

Realizing autonomous inspection, such as that of power distribution lines, through unmanned aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy and safety of the operation; however, many technical problems, such as those pertaining to the precise positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed strategy, each vehicle provides its position information to the other, which ensures a safe inspection process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating the feasibility of the proposed approach.

Reconstruction of Power Pylons From LiDAR Point Clouds Based on Structural Segmentation and Parameter Estimation

The reconstruction of 3-D models of power pylons from light detection and ranging (LiDAR) data plays an important role in power transmission safety. However, accurate reconstruction of power pylon models still faces challenges, e.g., complex structures, missing data, and occlusion. In this letter, a novel four-component segmentation method is proposed for reconstructing power pylon models. In the proposed method, the pylon components of the pylon head, pylon body, cross-arms, and pedestal are first defined in terms of the common features and functionality of each component. Then, these four components are each segmented and identified based on their position and shape features from the raw point cloud. An improved approach based on Metropolis-Hastings sampling and a simulated annealing algorithm is proposed to estimate the model parameters. Based on the estimated parameters, the 3-D shapes of the individual components are reconstructed and stacked to form the whole pylon model. Experimental results show that our methods are able to reconstruct pylons with complex-shaped heads and multiple cross-arms, with an average reconstruction error of less than 0.3 m. Compared with the standard Metropolis-Hastings algorithm with annealing, the parameter estimation process in our strategy improves the computational efficiency by 7.54%.

Mitigation of conductor line galloping by a direct cable-connection to non-conductive composite power pylons

Steel lattice towers with suspended insulator strings are typically used to carry high-voltage overhead transmission lines. The installation of non-conductive power pylons made of glass fibre reinforced plastics enables a direct cable-pylon connection, as the composite structure acts as an unibody insulator. At the same time, wind-induced vibrations, such as the severe cable vibration phenomenon galloping, will consequently be directly transferred to the slender composite mast structure, potentially leading to extensive damage. The aim of the study is therefore to investigate the galloping behaviour of iced conductor lines with regard to different cable support conditions. Furthermore, additional damping in the composite power pylon structure is assumed to mitigate conductor line galloping and therefore reduce the risk of phase flash-overs between adjacent conductor lines. A numerical galloping simulation is carried out in order to evaluate the effect of a rigid cable-pylon connection with enhanced damping properties on the cable vibration amplitudes. A pylon-cable system, consisting of 3×300 m spans, is investigated. It was found that the support conditions of the conductor lines have a significant influence on the galloping mode, the vibration amplitudes and the orientation of the characteristic galloping ellipse. The addition of damping to the pylon decreases the vibration amplitudes slightly and leads to a re-orientation of the galloping ellipse.

A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data

Object reconstruction from airborne LiDAR data is a hot topic in photogrammetry and remote sensing. Power fundamental infrastructure monitoring plays a vital role in power transmission safety. This paper proposes a heuristic reconstruction method for power pylons widely used in high voltage transmission systems from airborne LiDAR point cloud, which combines both data-driven and model-driven strategies. Structurally, a power pylon can be decomposed into two parts: the pylon body and head. The reconstruction procedure assembles two parts sequentially: firstly, the pylon body is reconstructed by a data-driven strategy, where a RANSAC-based algorithm is adopted to fit four principal legs; secondly, a model-driven strategy is used to reconstruct the pylon head with the aid of a predefined 3D head model library, where the pylon head’s type is recognized by a shape context algorithm, and their parameters are estimated by a Metropolis–Hastings sampler coupled with a Simulated annealing algorithm. The proposed method has two advantages: (1) optimal strategies are adopted to reconstruct different pylon parts, which are robust to noise and partially missing data; and (2) both the number of parameters and their search space are greatly reduced when estimating the head model’s parameters, as the body reconstruction results information about the original point cloud, and relationships between parameters are used in the pylon head reconstruction process. Experimental results show that the proposed method can efficiently reconstruct power pylons, and the average residual between the reconstructed models and the raw data was smaller than 0.3 m.

Power pylon detection and monocular depth estimation from inspection UAVs

Purpose This paper aims to present a new vision-based approach for both the identification and the estimation of the relative distance between the unmanned aerial vehicle (UAV) and power pylon. Autonomous power line inspection using small UAVs, has been the focus of many research works over the past couple of decades. Automatic detection of power pylons is a primary requirement to achieve such autonomous systems. It is still a challenging task due to the complex geometry and cluttered background of these structures. Design/methodology/approach The identification solution proposed, avoids the complexity of classic object recognition techniques. Instead of searching the whole image for the pylon template, low-level geometric priors with robust colour attributes are combined to remove the pylon background. The depth estimation, on the other hand, is based on a new concept which exploits the ego-motion of the inspection UAV to estimate its distance from the pylon using just a monocular camera. Findings An algorithm is tested on a quadrotor UAV, using different kinds of metallic power pylons. Both simulation and real-world experiments, conducted in different backgrounds and illumination conditions, show very promising results. Research limitations/implications In the real tests carried out, the Inertial Navigation System (INS) of the vehicle was used to estimate its ego-motion. A more reliable solution should be considered for longer distances, by either fusing INS and global positioning system data or using visual navigation techniques such as visual odometry. Originality/value A simple yet efficient solution is proposed that allows the UAV to reliably identify the pylon, with still a low processing cost. Considering a monocular solution is a major advantage, given the limited payload and processing power of such small vehicles.