The Open Electrical & Electronic Engineering Journal




ISSN: 1874-1290 ― Volume 13, 2019

Comparison of Neural Networks and Logistic Regression in Assessing the Occurrence of Failures in Steel Structures of Transmission Lines



A.C.G Bissacot1, *, S.A.B Salgado2, P.P Balestrassi1, *, A.P Paiva1, A.C Zambroni Souza2, R. Wazen3
1 Institute of Industrial Engineering - Federal University of Itajuba, Brazil
2 Institute of Electrical Engineering - Federal University of Itajuba, Brazil
3 Companhia Paranaense de Energia (COPEL), Brazil

Abstract

In this work, we evaluate the probability of falling metal structures from transmission lines. It is our objective to extract knowledge about which variables influence the mechanical behavior of the operating lines and can be used to diagnose potential falling towers. Those pieces of information can become a basis for directing the investments of reinforcement structures, avoiding the occurrence of long turn offs and high costs as a consequence of damage to towers of transmission lines. The results are obtained using the history of 181 metal structures currently in operation in the state of Paraná/Brazil. For the classification of transmission lines susceptible to failures it is proposed to identify the most likely lines considering the following parameters: operating voltage, wind and relief of the region, air masses, temperature, land type, mechanical capacity, function and foundation structure. The classic technique of classifying binary events used in this type of problem is the logistic regression (LR). The more recent technique for classification, using Artificial Neural Networks (ANN) can also be applied. The results are compared through the area under receiver operating characteristics (ROC) curves.

Keywords: Artificial Neural Networks, Fall of Metal Structures, Logistic Regression, ROC Curves, Transmission Lines.


Article Information


Identifiers and Pagination:

Year: 2016
Volume: 10
First Page: 11
Last Page: 26
Publisher Id: TOEEJ-10-11
DOI: 10.2174/1874129001610010011

Article History:

Received Date: 28/3/2015
Revision Received Date: 17/6/2015
Acceptance Date: 8/8/2015
Electronic publication date: 17/3/2016
Collection year: 2016

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© Bissacot et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.


* Address correspondence to these authors at the Institute of Industrial Engineering - Federal University of Itajuba, Brazil; Tel: +55 35 88776958; E-mail: ppbalestrassi@gmail.com


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