An optimal radial basis function neural network for fault location in a distribution network with high penetration of DG units

Hadi Zayandehroodi, Azah Mohamed, Masoud Farhoodnea, Marjan Mohammadjafari

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Due to environmental concerns and growing cost of fossil fuel, high levels of distributed generation (DG) units have been installed in power distribution systems. However, with the installation of DG units in a distribution system, many problems may arise such as increase and decrease of short circuit levels, false tripping of protective devices and protection blinding. This paper presents an automated and accurate fault location method for identifying the exact faulty line in the test distribution network with high penetration level of DG units by using the Radial Basis Function Neural Network with Optimum Steepest Descent (RBFNN-OSD) learning algorithm. In the proposed method, to determine the fault location, two RBFNN-OSD have been developed for various fault types. The first RBFNN-OSD is used for predicting the fault distance from the source and all DG units while the second RBFNN is used for identifying the exact faulty line. Several case studies have been simulated to verify the accuracy of the proposed method. Furthermore, the results of RBFNN-OSD and RBFNN with conventional steepest descent algorithm are also compared. The results show that the proposed RBFNN-OSD can accurately determine the location of faults in a test given distribution system with several DG units.

Original languageEnglish
Pages (from-to)3319-3327
Number of pages9
JournalMeasurement: Journal of the International Measurement Confederation
Volume46
Issue number9
DOIs
Publication statusPublished - 2013

Fingerprint

Distributed Generation
Electric fault location
Steepest Descent
Radial Basis Function Neural Network
Distribution Network
Distributed power generation
descent
Electric power distribution
Penetration
Fault
penetration
Neural networks
Unit
Distribution System
Descent Algorithm
fossil fuels
Fossil fuels
Power Distribution
Line
Short circuit currents

Keywords

  • Coordination
  • Distributed generation (DG)
  • Distribution network
  • Fault location
  • Neural network
  • Protection
  • RBFNN-OSD

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Applied Mathematics

Cite this

An optimal radial basis function neural network for fault location in a distribution network with high penetration of DG units. / Zayandehroodi, Hadi; Mohamed, Azah; Farhoodnea, Masoud; Mohammadjafari, Marjan.

In: Measurement: Journal of the International Measurement Confederation, Vol. 46, No. 9, 2013, p. 3319-3327.

Research output: Contribution to journalArticle

Zayandehroodi, Hadi ; Mohamed, Azah ; Farhoodnea, Masoud ; Mohammadjafari, Marjan. / An optimal radial basis function neural network for fault location in a distribution network with high penetration of DG units. In: Measurement: Journal of the International Measurement Confederation. 2013 ; Vol. 46, No. 9. pp. 3319-3327.
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