Cited by 95Power Line Inspection RobotsInfrastructure Maintenance and MonitoringAdvanced Neural Network Applications

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Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks

Hosseini, Mohammad Mehdi, Umunnakwe, Amarachi, Parvania, Masood, Tasdizen, Tolga·2020·IEEE Transactions on Smart Grid

DOI 10.1109/tsg.2020.2970156

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Abstract

Damage estimation is part of daily operation of power utilities, often requiring a manual process of crew deployment and damage report to quantify and locate damages. Advancement in unmanned aerial vehicles (UAVs) as well as real-time communication and learning technologies could be harnessed towards efficient and accurate automation of this process. This paper develops a model to automate the process of estimating and localizing damages in power distribution poles, which utilizes the images taken by UAVs transferred in real-time to an intelligent damage classification and estimation (IDCE) unit. The IDCE unit integrates four convolutional neural networks to learn the states of poles from images, extract the image characteristics, and train an automated intelligent tool to replace manual fault location and damage estimation. The proposed model first determines the type of pole damages, including falling and burning, and then estimates the percentage of damage in each type. The IDCE unit also localizes damages in the poles by locating possible burning or arcing parts. A data set of 1615 images is utilized to train, validate and test the proposed model, which demonstrates high accuracy of the model in classifying and estimating damages in distribution poles.

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Authors: Hosseini, M. M., Umunnakwe, A., Parvania, M., & Tasdizen, T.
Affiliations: University of Utah (US)
ORCID: Hosseini, Mohammad Mehdi
Umunnakwe, Amarachi
Parvania, Masood
Tasdizen, Tolga
Venue: IEEE Transactions on Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-07
Volume: 11
Issue: 4
Pages: 3325-3333
ISSN: 1949-3053 (ISSN-L)
1949-3061
DOI: 10.1109/tsg.2020.2970156
OpenAlex: W3004200729
Cited by: 95
References: 28
Citations by year: 2026: 16 · 2025: 15 · 2024: 15 · 2023: 15 · 2022: 14 · 2021: 14 · 2020: 6
FWCI: 6.03
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Smart Grid (publishedVersion)
Funders: Office of Naval Research (N000141812395)
Topics: Physical Sciences › Engineering › Mechanical Engineering › Power Line Inspection Robots
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: damage estimation, unmanned aerial vehicles, convolutional neural networks, fault location, real-time image analysis, power utility automation

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Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks

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