Applying Artificial Neural Networks and Nonlinear Optimization Techniques to Fault Location in Transmission Lines—Statistical Analysis

Simone A. Rocha, Thiago G. Mattos, Rodrigo T. N. Cardoso and Eduardo G. Silveira
Additional contact information
Simone A. Rocha: Program in Mathematical and Computational Modeling, Federal Center for Technological Education of Minas Gerais, Belo Horizonte 30180-001, Brazil
Thiago G. Mattos: Program in Mathematical and Computational Modeling, Federal Center for Technological Education of Minas Gerais, Belo Horizonte 30180-001, Brazil
Rodrigo T. N. Cardoso: Program in Mathematical and Computational Modeling, Federal Center for Technological Education of Minas Gerais, Belo Horizonte 30180-001, Brazil
Eduardo G. Silveira: Department of Electrical Engineering, Federal Center for Technological Education of Minas Gerais, Belo Horizonte 30180-001, Brazil

Energies, 2022, vol. 15, issue 11, 1-24

Abstract: This study presents applications of artificial neural networks and nonlinear optimization techniques for fault location in transmission lines using simulated data in an electromagnetic transient program and actual data occurring in transmission lines. The localization is performed by a modular structure of 4 neural networks and by the minimization of objective functions descriptive of the problem, defined according to the parameters of the line and the type of short circuit, submitted to the methods Quasi-Newton, Ellipsoidal, and Real Polarized Genetic Algorithm. The results obtained are compared statistically with those of a classical analytical method. The analysis of the variance of location errors presented by the methods revealed, with 5% significance, statistical evidence that allowed the conclusion that the type of method used affects fault location indication. In simulated scenarios, minor errors were obtained with the neural network and larger with the analytical method. For field oscillographic, the largest errors were in the neural network; there is no evidence to reject the equality between the results of the analytical method and the nonlinear optimization techniques. The Tukey test identified no differences between the nonlinear optimization methods applied to the proposed objective functions, but the low computational cost associated with the Quasi-newton method highlights it. The nonlinear optimization methods used for the localization function proved to be promising for application in companies that operate electrical systems, providing localization errors similar to those presented by the classical analytical method.

Keywords: fault location; transmission line; artificial neural network; nonlinear optimization; statistical analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/11/4095/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/11/4095/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:11:p:4095-:d:830440

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().