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International Journal of Electrical Power & Energy Systems
Article . 2021 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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UAV-lidar aids automatic intelligent powerline inspection

Authors: Hongcan Guan; Xiliang Sun; Yanjun Su; Tianyu Hu; Haitao Wang; Heping Wang; Chigang Peng; +1 Authors

UAV-lidar aids automatic intelligent powerline inspection

Abstract

Abstract In recent decades, a substantial increase in electricity demand has put pressure on powerline systems to ensure an uninterrupted power supply. In order to prevent power failures, timely and thorough powerline inspections are needed to detect possible anomalies in advance. In the past few years, the emerging unmanned aerial vehicle (UAV)-mounted sensors (e.g. light detection and ranging/lidar, optical cameras, infrared cameras, and ultraviolet cameras) have provided rich data sources for comprehensive and accurate powerline inspections. A challenge that still hinders the use of UAVs in powerline inspection is that their operation is highly dependent on the pilot’s experience, which may pose risks to the safety of the powerline system and reduce inspection efficiency. An intelligent automatic inspection solution could overcome the limitations of current UAV-based inspection solutions. The main objective of this paper is to provide a contemporary look at the current state-of-the-art UAV-based inspections as well as to discuss a potential lidar-supported intelligent powerline inspection concept. Overall, standardized protocols for lidar-supported intelligent powerline inspections include four data analysis steps, i.e., point cloud classification, key point extraction, route generation, and fault detection. To demonstrate the feasibility of the proposed concept, we implemented a workflow using a dataset of 3536 powerline spans, showing that the inspection of a single powerline span could be completed in 10 min with only one or two technicians. This demonstrates that lidar-supported intelligent inspection can be used to inspect a powerline system with extremely high efficiency and low costs.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
158
Top 1%
Top 1%
Top 1%
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