Automatic generation of power distribution line ledgers using RFID and deep learning techniques

To facilitate the automatic generation of power distribution line ledgers, a technique harnessing both Radio Frequency Identification (RFID) and deep learning is introduced. Drones equipped with RFID tools are utilised to capture information from power distribution pylons, culminating in the creation of a geographical map of the power distribution line, thereby providing essential data support for automatic ledger generation. Subsequent to this, various models are employed to detect images of power distribution line equipment, with the Yolov5-s model being specifically employed for the identification of equipment on distribution line poles. Detection outcomes are subsequently displayed on the geographical map of the distribution line, culminating in the automatic generation of the power distribution line ledger. Results from this study indicate that the introduced method can effectively and swiftly generate power distribution line ledgers.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1117/12.3034205
Primary Topic
Power Line Communications and Noise
Type
article
Field-Weighted Citation Impact
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article

Automatic generation of power distribution line ledgers using RFID and deep learning techniques

Shiyang Zhou, Wenbin Wang, Jianyong Liu
Power Line Communications and Noise
article

Automatic generation of power distribution line ledgers using RFID and deep learning techniques

Shiyang Zhou, Wenbin Wang, Jianyong Liu
article en

Abstract

To facilitate the automatic generation of power distribution line ledgers, a technique harnessing both Radio Frequency Identification (RFID) and deep learning is introduced. Drones equipped with RFID tools are utilised to capture information from power distribution pylons, culminating in the creation of a geographical map of the power distribution line, thereby providing essential data support for automatic ledger generation. Subsequent to this, various models are employed to detect images of power distribution line equipment, with the Yolov5-s model being specifically employed for the identification of equipment on distribution line poles. Detection outcomes are subsequently displayed on the geographical map of the distribution line, culminating in the automatic generation of the power distribution line ledger. Results from this study indicate that the introduced method can effectively and swiftly generate power distribution line ledgers.

Affordable and clean energy
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Power Line Communications and Noise
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