Search Results (57)

Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors. Full article

The safe operation of power transmission lines is critical for ensuring the stability of the power supply, especially given the increasing frequency of extreme weather events and the risks posed by tree growth. This study proposes a novel method for detecting and predicting the tree barrier risks on transmission lines using Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) technology. The method employs point cloud classification to effectively separate ground, conductor, tower, and vegetation points, followed by 3D reconstruction of the power lines using the catenary equation. Tree growth models are integrated with measured data to predict future tree barrier risks. The experimental results demonstrate that the point-cloud-based method detects 31 tree barriers, with an RMSE of 0.08 m, while the 3D-reconstruction-based method detects 32 tree barriers, with an RMSE of 0.04 m, indicating its higher accuracy. The Cloth Simulation Filter (CSF) ground point classification method achieved the lowest roughness (1.5%), mean error (0.147 m), and RMSE (0.174 m), proving its effectiveness for flat terrain. Additionally, the assisted seed point individual tree segmentation method extracted tree height with high accuracy (R² = 0.84, RMSE = 1.01 m). This study predicts an average tree growth rate of 0.248 m/year over the next five years, identifying a new tree barrier at the coordinates 30°15′16.64″ N, 119°43′16.01″ E. This method enhances the efficiency and accuracy of transmission line inspections, supporting both power line safety and sustainable forest management. Its findings provide a robust technical approach to improving power line operations and forest resource utilization. Full article

Electric power transmission lines both cause and are impacted by wildfires and fuel breaks are crucial to mitigate wildfire hazard along and in conjunction with these infrastructures. Information about fuel dynamics is crucial for planning and maintaining fuel treatments, namely, to define their frequency. We sampled mechanically treated power line corridors representative of wide variation in climate, soil, and plant communities in Portugal and at different times since treatment. Non-destructive methods were used to assess ground cover and height per fuel stratum and the corresponding phytovolumes and fine fuel loads were calculated. Variability in fuel metrics was described by fitting logistic generalized linear models or linearized power functions based on time since disturbance and categorical variables for the effect of regeneration strategy, soil-richness structure, and climate. Time since treatment dominated fuel abundance and recovery was faster in communities of obligate resprouters in comparison with obligate or facultative seeders and in light- versus heavy-textured soils. No apparent effect of local climate was found given the short-lived impact of fuel treatments under the productive regional Mediterranean climate. The results provide a decision-making basis to refine the current treatment periodicity. Mechanical-treatment intervals of 3–5 years or 6–10 years are advised, respectively, for wildfire control and to minimize infrastructure damage. Full article

Electrification of the transport sector introduces operational issues in the electricity distribution network, such as excessive voltage deviation, substation overloading, and adverse power quality impacts on other network loads. These concerns are expected to grow as electrification expands to incorporate heavy vehicles such as trucks and buses due to their greater energy requirements and higher charging loads. Two strategies are proposed to support medium- and heavy-duty chargers which address their high power demand and mitigate power quality disturbances and the overloading of substations. The first is a dedicated feeder connected at the secondary of the substation directly to the charging station which aims to reduce the impact of high load on other customers. The second is the addition of a dedicated substation that solely provides power for charging stations in major corridors, alleviating stress on existing zone substations. Hosting capacity is measured using a voltage deviation index, describing the deviation in line voltage, which should experience a sag of no more than 6% of the nominal voltage, and a substation charging capacity index, describing the available capacity of each zone substation as a ratio of its total power capacity. Verification of the proposed strategies was performed on an MV-LV distribution network representative of an industrial Australian town with heavy-vehicle charging. Results showed that the network could handle ten 250 kW chargers, which was tripled to 35 with a dedicated feeder. The dedicated feeder alternatively allowed up to 10 megawatt-scale chargers, which was again tripled when a dedicated substation was introduced. Full article

Accurate semantic segmentation in transmission corridor scenes is crucial for the maintenance and inspection of power infrastructure, facilitating the timely detection of potential hazards. In this study, we propose SA-KPConv, an advanced segmentation model specifically designed for transmission corridor scenarios. Traditional approaches, including Random Forest and point-based deep learning models such as PointNet++, demonstrate limitations in segmenting critical infrastructure components, particularly power lines and towers, primarily due to their inadequate capacity to capture complex spatial relationships and local geometric details. Our model effectively addresses these challenges by integrating a spatial attention module with kernel point convolution, enhancing both global context and local feature extraction. Experiments demonstrate that SA-KPConv outperforms state-of-the-art methods, achieving a mean Intersection over Union (mIoU) of 89.62%, particularly excelling in challenging terrains such as mountainous areas. Ablation studies further validate the significance of our model’s components in enhancing overall performance and effectively addressing class imbalance. This study presents a robust solution for semantic segmentation, with considerable potential for monitoring and maintaining power infrastructure. Full article

Overhead transmission lines are widely deployed across both mountainous and plain areas and serve as a critical infrastructure for China’s electric power industry. The rapid advancement of three-dimensional (3D) laser scanning technology, with airborne LiDAR at its core, enables high-precision and rapid scanning of the detection area, offering significant value in identifying safety hazards along transmission lines in complex environments. In this paper, five transmission lines, spanning a total of 160 km in the mountainous area of Sanmenxia City, Henan Province, China, serve as the primary research objects and generate several insights. The location and elevation of each power tower pole are determined using an Unmanned Aerial Vehicle (UAV), which assesses the direction and elevation changes in the transmission lines. Moreover, point cloud data of the transmission line corridor are acquired and archived using a UAV equipped with LiDAR during variable-height flight. The data processing of the 3D laser point cloud of the power corridor involves denoising, line repair, thinning, and classification. By calculating the clearance, horizontal, and vertical distances between the power towers, transmission lines, and other surface features, in conjunction with safety distance requirements, information about potential hazards can be generated. The results of detecting these five transmission lines reveal 54 general hazards, 22 major hazards, and an emergency hazard in terms of hazards of the vegetation type. The type of hazard in the current working condition is mainly vegetation, and the types of cross-crossing hazards are power lines and buildings. The detection results are submitted to the local power department in a timely manner, and relevant measures are taken to eliminate hazards and ensure the normal supply of power resources. The research in this paper will provide a basis and an important reference for identifying the potential safety hazards of transmission lines in Henan Province and other complex environments and solving existing problems in the manual inspection of transmission lines. Full article

Transmission lines are essential carriers for the transmission of electrical energy, and their safe operation is fundamental to ensuring reliable power supply. However, the environment of transmission line corridors is complex and variable, posing numerous risks of external damage. With the development of smart power systems, information technologies such as video monitoring provide effective means for monitoring and managing external damage to transmission lines. Nonetheless, the current technology for identifying external damage and safe distances for transmission lines is weak, leading to frequent false alarms and low efficiency in practical applications. To address this issue, this paper systematically reviews and summarizes the progress of distance recognition technology in power systems. First, it outlines the spatial distance perception performance requirements for models in transmission line corridor scenarios and summarizes the spatial distance data characteristics of these corridors. Second, the paper categorizes existing common safety distance recognition methods from the perspectives of active and passive ranging, analyzing their advantages and disadvantages in the context of transmission line corridors. Finally, this paper summarizes the types and performance characteristics of current deep learning-based distance recognition methods and proposes a potential application framework for these methods in transmission line corridor scenarios. It also discusses the future application prospects and challenges faced by this framework. Full article

The electric field of transmission lines has serious negative impacts on residents’ production and life with the expansion of high voltage engineering. In order to study the influence of trees on the electric field of ultra-high voltage transmission lines, this paper conducted three-dimensional simulation calculations of the power frequency electric field of transmission lines based on the tree quantity and distribution. Firstly, in order to study the pattern of electric field strength distribution in transmission lines, the electric field strengths of transmission lines of different voltage levels were compared; the maximum-power-frequency electric field intensity of ultra-high voltage transmission lines occurs below the edge conductor. Secondly, by changing the number of trees, it was concluded that the electric field strength below the edge conductor gradually decreases with the number of trees. Finally, the maximum electric field strength value at 1.5 m below the edge conductor and the width of the transmission corridor decreased by changing the layout of the trees. The results show that studying the impact of a tree’s electromagnetic parameters on the power frequency electric field strength under transmission lines can help reduce the electric field strength and decrease the width of transmission corridors, which is of great significance for line design and cost savings. Full article

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network’s ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks. Full article

The high-speed railway project and the ultra-high-voltage transmission project represent two crucial components of China’s “new infrastructure”. As the construction of these two projects progresses rapidly, it is inevitable that instances of intersections will occur. Extreme conditions may cause damage to ultra-high voltage transmission cables. When a high-speed train passes by an ultra-high voltage transmission line, it poses a serious safety hazard. To address this issue, engineering examples were utilized to examine the protection structure scheme, protection distance, protection load, and construction procedures when a high-speed railway intersects a 1000 KV ultra-high voltage transmission line. A shed structure form and construction method for the electric power protection were proposed to ensure the safe operation of the high-speed railway while also achieving the safe and rapid construction of the high-speed railway protection structure in the safety zone of the approaching 1000 kV ultra-high voltage transmission line. The results indicated that the protection of high-speed railway crossings and 1000 kV ultra-high voltage transmission lines primarily focuses on line-break protection. The concrete shed structure with a straight wall and a flat roof was designed to meet the requirements of high-speed railway crossings. The line-break protection method enables the construction of an automatic warning protection corridor and a complete movable trolley quickly and safely within the safety zone near the transmission line. The implementation effect is, therefore, positive. It can be used as a reference point for other projects of a similar nature. Full article

Insulators are key components to ensure the normal operation of power facilities in transmission corridors. Existing insulator identification methods mainly use image data and lack the acquisition of three-dimensional information. This paper proposes an efficient insulator extraction method based on UAV (unmanned aerial vehicle) LiDAR (light detection and ranging) point cloud, using five histogram features: horizontal density (HD), horizontal void (HV), horizontal width (HW), vertical width (VW) and vertical void (VV). Firstly, a voxel-based method is employed to roughly extract power lines and pylons from the original point cloud. Secondly, the VV histogram is used to categorize the pylons into suspension and tension types, and the HD histogram is used to locate the tower crossarm and further refine the roughly extracted powerlines. Then, for the suspension tower, insulators are segmented based on the HV histogram and HD difference histogram. For the tension tower, the HW histogram is used to recognize the jumper conductor (JC) and transmission conductor (TC) from the power line. The HW histogram and VW histogram are used to extract the tension insulator in the TC and suspension insulator in the JC, respectively. Finally, considering the problem of setting a suitable grid width when constructing the feature histogram, an adaptive method of multi-scale histograms is proposed to refine the extraction result. Two 220 kV long transmission lines are used for the validation, and the overall object-based accuracy for suspension and tension towers are 100% and 97.3%, respectively. Compared with the point feature-based method, the mean F1 score of the proposed method improved by 0.3, and the runtime for each tower is within 2 s. Full article

Recently, significant attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) because of its economic feasibility. At the same time, the growing demand for electricity and the increasing penetration of renewable energy sources have prompted the electric power industry to explore methods to optimize the use of the existing grid infrastructure. Dynamic thermal line rating (DTLR) is a technique that allows transmission lines to operate at their maximum capacity, considering their real-time operating conditions. The majority of existing research on this topic has focused predominantly on employing DTLR in alternating current systems and exploring their applications. This study presents a novel approach by applying DTLR to HVDC transmission corridors, with the aim of maximizing the utilization of their capacity and facilitating increased integration of renewable energy. The performance of the proposed approach is evaluated by conducting a case study for an HVDC transmission line in Alberta, Canada. On average, the mean increase in ampacity above the static rating is 64% during winter and 34% during summer. This additional capacity can be used to integrate wind energy, replacing coal-fired generation. This leads to a significant reduction in greenhouse gas emissions, also quantified in this contribution. Full article

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 $L^{\infty }$; (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. Full article

Vegetation encroachment in power line corridors remains a major challenge for modern energy-dependent societies, as it can cause power outages and lead to significant financial losses. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for monitoring infrastructure, owing to their ability to acquire high-resolution overhead images of these areas quickly and affordably. However, accurate segmentation of the vegetation encroachment in this imagery is a challenging task, due to the complexity of the scene and the high pixel imbalance between the power lines, the vegetation and the background classes. In this paper, we propose a deep learning-based approach to tackle this problem caused by the original and different geometry of the objects. Specifically, we use DeepLabV3, U-Net and a modified version of the U-Net architecture with VGG-16 weights to train two separate models. One of them segments the dominant classes, the vegetation from the background, achieving an IoU of 0.77. The other one segments power line corridors from the background, obtaining an IoU of 0.64. Finally, ensembling both models into one creates an “encroachment” zone, where power lines and vegetation are intersected. We train our models using the Vegetation Encroachment in Power Line Corridors dataset (VEPL), which includes RGB orthomosaics and multi-label masks for segmentation. Experimental results demonstrate that our approach outperforms individual networks and original prominent architectures when applied to this specific problem. This approach has the potential to significantly improve the efficiency and accuracy of vegetation encroachment monitoring using UAV, thus helping to ensure the reliability and sustainability of power supply. Full article

The electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialisation, population growth, urbanisation and, of course, the evolution of modern technologies in this 4th industrial revolution era. Such a rapid increase in energy demand introduces a huge challenge into the power system, which has paved way for network operators to seek alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such an increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a DC TNEP model that minimises the capital costs of additional transmission lines, network reinforcements, generator operation costs and the costs of renewable energy penetration, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model was tested in several IEEE test systems in multi-period scenarios. We also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variable decomposition techniques. Moreover, we intend to provide additional recommendations in terms of in which particular year (within a 20 year planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. This is achieved by running incremental period simulations from the base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time, even for large power grid system. Full article