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A Review on State-of-the-Art Power Line Inspection Techniques

IEEE Transactions on Instrumentation and Measurement · 2020 · Vol. 69(12) · pp. 9350–9365
Lei YangJunfeng FanYanhong LiuEn LiJinzhu PengZize Liang

Abstract

With the fast development of smart grid, the power line mileage and power equipments get rapid growth. The contradiction between the large number of maintenance equipments and the small number of maintenance workers becomes increasingly prominent. Meanwhile, the traditional maintenance mode has the disadvantages of over or under maintenance, which will lead to the increasing failure risk of power transmission system. Faced with these issues, to enhance the intelligence and automation level of power line inspection, many researchers have devoted much effort to the research of automatic power line inspection and some state-of-the-art techniques about power line inspection are proposed to improve the inspection efficiency and quality, such as unmanned aerial vehicle (UAV), image processing, deep architecture, and so on. In this article, we analyzed and summarized the state-of-the-art techniques on power line inspection to provide a valuable reference for the researchers engaged in the smart grid. First, the common inspection tasks on power line inspection are reviewed. Second, the existing inspection platforms are examined in this article. Also, the advantages and disadvantages of different platforms are analyzed accordingly. Third, faced with different inspection tasks, different inspection sensors are integrated into the inspection platforms for data collection. Therefore, the common sensors on inspection platforms are discussed. Finally, to realize automatic power line inspection, different inspection methods are proposed or improved, and these advanced inspection methods are surveyed and discussed.

Power Line Inspection RobotsRemote Sensing and LiDAR ApplicationsAdvanced Neural Network ApplicationsAutomated X-ray inspectionAutomationAutomated optical inspectionComputer scienceReliability engineeringVisual inspectionLine (geometry)State (computer science)Electric power transmissionEngineering

Funding

  • National Natural Science Foundation of China
Citations
387
FWCI
16.98
field-weighted impact
References
159
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100%
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References
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Deep learning
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