Machine Learning Approach to Detect Faults in Anchor Rods of Power Transmission Lines

Detecting faults on the anchor rods of power transmission line towers is an essential procedure for guaranteeing the safety, operability, and availability of the electric power system. Such a measure aims to minimize the risk of tower collapse and the associated costs due to the lack of energy, inju...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE antennas and wireless propagation letters Vol. 18; no. 11; pp. 2335 - 2339
Main Authors:	Barbosa, Douglas Contente Pimentel, Tarrago, Vinicius Leal, de Medeiros, Luiz Henrique Alves, de Melo, Marcos Tavares, da Silva Lourenco Novo, Lauro Rodrigo Gomes, Coutinho, Marcelo de Sa, Alves, Marcelo Macedo, Lott Neto, Henrique Baptista Duffles Teixeira, Gama, Paulo Henrique Ramalho Pereira, dos Santos, Renan Guilherme Matias
Format:	Journal Article
Language:	English
Published:	New York IEEE 01-11-2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Anchors Artificial intelligence Circuit faults Classification algorithms Collapse Computational modeling Computer simulation Electric power systems Electricity distribution electromagnetic (EM) measurements Electromagnetic scattering Fault detection Machine learning machine learning (ML) Machine learning algorithms Nondestructive testing Power lines Power transmission lines Rods scattering parameters systems simulation Task analysis Transmission line measurements Transmission lines
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Detecting faults on the anchor rods of power transmission line towers is an essential procedure for guaranteeing the safety, operability, and availability of the electric power system. Such a measure aims to minimize the risk of tower collapse and the associated costs due to the lack of energy, injuries to people, and damage to the reputation of the companies involved. In this letter, we report an alternative noninvasive and nondestructive detection technique through the acquisition of electromagnetic scattering parameters. A novel machine learning (ML) approach is proposed in order to achieve the advantages of such a technique and to deal with the respective drawbacks due to the complexity of signals acquired. A strategy to artificially increase the number of relevant samples in the dataset through the simulations of computational models is performed. Different ML classification algorithms are compared in the task of detecting structural faults in anchor rods in order to define the most suitable to this application. Finally, experimental results show that the methodology proposed achieves a high detection accuracy, surpassing other known methods.
ISSN:	1536-1225 1548-5757
DOI:	10.1109/LAWP.2019.2932052