A review on the aging performance of direct current cross-linked polyethylene insulation materials

Zhang, Chengcheng; Li, Chunyang; Zhao, Hong; Han, Baozhong

doi:10.1109/icpadm.2015.7295368

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it typically comprises many vital components, including the tower body, cross arms, insulator strings, lightning lines, and additional auxiliary equipment [3][4][5]. The transmission tower is a complex system; the structures complement each other to undertake the function of power transportation jointly, and some parts are easily affected by internal and external environmental impact and aging damage, thus causing power accidents resulting in economic losses [6]. Therefore, it is Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Target Point Localization for UAV Inspection of Point Cloud Transmission Towers

Li,

Chen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

UAV transmission tower inspection is the use of UAV technology for regular inspection and troubleshooting of towers on transmission lines, which helps to improve the safety and reliability of transmission lines and ensures the stability of the power supply. From the traditional manual tower boarding to the current way of manually selecting target camera shooting points from 3D point clouds to plan the inspection path of the UAV, operational efficiency has drastically improved. However, indoor planning work is still labor-consuming and expensive. In this paper, a deep learning-based point cloud transmission tower segmentation (PCTTS) model combined with the corresponding target point localization algorithm is proposed for automatic segmentation of transmission tower point cloud data and automatically localizing the key inspection component as the target point for UAV inspection. First, we utilize octree sampling with unit ball normalization to simplify the data and ensure translation invariance before putting the data into the model. In the feature extraction stage, we encode the point set information and combine Euclidean distance and cosine similarity features to ensure rotational invariance. On this basis, we adopt multi-scale feature extraction, construct a local coordinate system, and introduce the offset-attention mechanism to enhance model performance further. Then, after the feature propagation module, gradual up-sampling is used to obtain the features of each point to complete the point cloud segmentation. Finally, combining the segmentation results with the target point localization algorithm completes the automatic extraction of UAV inspection target points. The method has been applied to six kinds of transmission tower point cloud data of part segmentation results and three kinds of transmission tower point cloud data of instance segmentation results. The experimental results show that the model achieves mIOU of 94.1% on the self-built part segmentation dataset and 86.9% on the self-built instance segmentation dataset, and the segmentation accuracy outperforms that of the methods for point cloud segmentation, such as PointNet++, DGCNN, Point Transformer, and PointMLP. Meanwhile, the experimental results of UAV inspection target point localization also verify the method’s effectiveness in this paper.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Target Point Localization for UAV Inspection of Point Cloud Transmission Towers

Li,

Chen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The power cable has been extensively used in power transmission and distribution systems due to its large ampacity and ability to be installed underground [1,2]. The cables have been used for more than 40 years of service in the developed countries, and the cable failure accidents frequently occur in large cities due to the aging and manufactured defects of cables during the long-term operation [3,4]. Therefore, it is necessary to develop an effective and efficient method to detect the defects and predict the aging rate based on the electrical tree model in the cable to maintain safe power transmission.…”

Section: Introductionmentioning

confidence: 99%

Charge-Simulation-Based Electric Field Analysis and Electrical Tree Propagation Model with Defects in 10 kV XLPE Cable Joint

Cui

2019

Energies

View full text Add to dashboard Cite

The most severe partial discharges and main insulation failures of 10 kV cross-linked polyethylene cables occur at the joint due to defects caused by various factors during the manufacturing and installation processes. The electric field distortion is analyzed as the indicator by the charge simulation method to identify four typical defects (air void, water film, metal debris, and metal needle). This charge simulation method is combined with random walk theory to describe the stochastic process of electrical tree growth around the defects with an analysis of the charge accumulation process. The results illustrate that the electrical trees around the metal debris and needle are more likely to approach the cable core and cause main insulation failure compared with other types of the defects because the vertical field vector to the cable core is significantly larger than the field vectors to other directions during the tree propagation process with conductive defects. The electric field was measured around the cable joint surface and compared with the simulation results to validate the calculation model and the measurement method. The air void and water film defects are difficult to detect when their sizes are less than 5 mm3 because the field distortions caused by the air void and water film are relatively small and might be concealed by interference. The proposed electric field analysis focuses on the electric field distortion in the cable joint, which is the original cause of the insulation material breakdown. This method identifies the defect and predicts the electrical tree growth in the cable joint simultaneously. It requires no directly attached or embedded sensors to impact the cable joint structure and maintains the power transmission during the detection process.

show abstract

Risks and Limitations Associated with XLPE Nanocomposites and Blends

Sarkari

Mohseni

Ebrahimi

2021

Materials Horizons: From Nature to Nanomaterials

View full text Add to dashboard Cite

A review on the aging performance of direct current cross-linked polyethylene insulation materials

Cited by 4 publications

References 24 publications

Deep Learning-Based Target Point Localization for UAV Inspection of Point Cloud Transmission Towers

Deep Learning-Based Target Point Localization for UAV Inspection of Point Cloud Transmission Towers

Charge-Simulation-Based Electric Field Analysis and Electrical Tree Propagation Model with Defects in 10 kV XLPE Cable Joint

Risks and Limitations Associated with XLPE Nanocomposites and Blends

Contact Info

Product

Resources

About