Electrical Equipment Identification Method With Aerial Inspection Image Based on Substation Grid Localization

The application of unmanned aerial vehicles (UAVs) in power system inspection is becoming increasingly widespread. Due to the variety of equipment, multiple transmission line bays, and high similarity among devices within substations, improving the efficiency of UAV inspection in substations is essential. In this paper, a method based on substation grid localization is proposed to associate UAV inspection images with power equipment, aiming to quickly archive UAV inspection photos and differentiate the target equipment inspected. Practical engineering demonstrations validate that the proposed method is applicable to both manual and autonomous inspections.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/icpre62586.2024.10768656
Citations
1
Primary Topic
Power Line Inspection Robots
Type
article
Field-Weighted Citation Impact
0.21

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article

Electrical Equipment Identification Method With Aerial Inspection Image Based on Substation Grid Localization

Zhewen Niu, Yihao Yang, Ning Chen, Yan Liu et al.
1 citations
Power Line Inspection Robots
0.21
article

Electrical Equipment Identification Method With Aerial Inspection Image Based on Substation Grid Localization

Zhewen Niu, Yihao Yang, Ning Chen, Yan Liu, Lu Shihong
article en
1 citations

Abstract

The application of unmanned aerial vehicles (UAVs) in power system inspection is becoming increasingly widespread. Due to the variety of equipment, multiple transmission line bays, and high similarity among devices within substations, improving the efficiency of UAV inspection in substations is essential. In this paper, a method based on substation grid localization is proposed to associate UAV inspection images with power equipment, aiming to quickly archive UAV inspection photos and differentiate the target equipment inspected. Practical engineering demonstrations validate that the proposed method is applicable to both manual and autonomous inspections.

Fujian Electric Power Survey & Design Institute (CN), Taiyuan University of Technology (CN)
Natural Science Foundation of Shanxi Province
Openalex Percentile: Top 48%
Power Line Inspection Robots
0.21
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