Power Line Simulation for Safety Distance Detection Using Point Clouds

Airborne LiDAR has been adopted as a powerful survey tool for overhead power transmission line (TL) cruising so that the operator can quickly search for, locate and eliminate the risk objects at the power line corridor scene. However, the TLs are moving objects, which positions are dynamically affec...

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Bibliographic Details
Main Authors: Sai Chang Zhang, Jun Zheng Liu, Zheng Niu, Shuai Gao, Hai Zhi Xu, Jie Pei
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9187816/
Description
Summary:Airborne LiDAR has been adopted as a powerful survey tool for overhead power transmission line (TL) cruising so that the operator can quickly search for, locate and eliminate the risk objects at the power line corridor scene. However, the TLs are moving objects, which positions are dynamically affected by working conditions (e.g. temperature variation, wind-induced conductor motion) while they are acquired by airborne LiDAR. The point clouds data acquired by airborne LiDAR only reflect the geometric relationship between the TL and its surrounding objects at the transient moment of the data acquisition. In order to overcome the shortcomings of the instantaneously acquired laser scanning data, this article presents an approach for simulating the dynamic TL shape under different working conditions based on mechanical computation of the overhead line. The proposed approach considers the tension variation of TL resulting from weather conditions, such as temperature, wind and ice, while simulates TL sag curve using the parabolic catenary equation combined with the tension. A performance evaluation was conducted over the TLs data with multiple voltage levels. Experiments results show that the proposed approach is effective for simulating the 3D shape of TL under different working conditions. The simulation error achieved was less than 0.65m and the maximum Diff-Ratio was about 1.57%. This provides a scientifically sound predicting and modeling approach for TL risk assessment and warning along the corridor.
ISSN:2169-3536