Design and Implementation of Using Lidar as a Safety Behavior Pre Control Device for Power Distribution Operations

Electric shock accidents remain a major safety concern for distribution workers. Recent advancements in video AI applications allow for detecting when workers cross safety lines, but determining their height and the spatial distance between them and live equipment is still a challenge. This article proposes a pre-control system using LiDAR, an edge processing module, and a warning module to ensure safe operations in power distribution scenarios. The system scans the area in real time, uses deep learning to identify objects like distribution stations, human bodies, high-voltage equipment, and transmission lines in point clouds, and calculates the distance between operators and high-voltage equipment. When this distance approaches or exceeds safety limits, the warning module issues voice alerts. Experimental results show that this system significantly reduces false alarms compared to video-based methods, accurately measures distances, and provides timely warnings, making it a practical solution for enhancing worker safety in power distribution operations.

Authors

Publication Details

Journal
Journal of Combinatorial Mathematics and Combinatorial Computing
Published
2024-12-31
DOI
https://doi.org/10.61091/jcmcc123-10
Primary Topic
Electrical Fault Detection and Protection
Type
article
Field-Weighted Citation Impact
0.00
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article

Design and Implementation of Using Lidar as a Safety Behavior Pre Control Device for Power Distribution Operations

Huiqing Wen, Xun Yue, Jonathan Li, Xiaobin Li
Journal of Combinatorial Mathematics and Combinatorial Computing
Electrical Fault Detection and Protection
article

Design and Implementation of Using Lidar as a Safety Behavior Pre Control Device for Power Distribution Operations

Huiqing Wen, Xun Yue, Jonathan Li, Xiaobin Li
article en

Abstract

Electric shock accidents remain a major safety concern for distribution workers. Recent advancements in video AI applications allow for detecting when workers cross safety lines, but determining their height and the spatial distance between them and live equipment is still a challenge. This article proposes a pre-control system using LiDAR, an edge processing module, and a warning module to ensure safe operations in power distribution scenarios. The system scans the area in real time, uses deep learning to identify objects like distribution stations, human bodies, high-voltage equipment, and transmission lines in point clouds, and calculates the distance between operators and high-voltage equipment. When this distance approaches or exceeds safety limits, the warning module issues voice alerts. Experimental results show that this system significantly reduces false alarms compared to video-based methods, accurately measures distances, and provides timely warnings, making it a practical solution for enhancing worker safety in power distribution operations.

Journal of Combinatorial Mathematics and Combinatorial ComputingVol. 123(1)
Openalex Percentile: Top 77%
Electrical Fault Detection and Protection
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