Automated fault location in a power system with distributed generations using radial basis function neural networks

H. Zayandehroodi, A. Mohamed, H. Shareef, M. Mohammadjafari

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

High penetration of Distributed Generation (DG) units will have unfavorable impacts on the traditional fault location methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This study presents an automated fault location method using Radial Basis Function Neural Network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is determined first by normalizing the fault currents of the main source. Then to determine the fault location, two RBFNNs have been developed for various fault types. The first RBFNN is used for detraining fault distance from each source and the second RBFNN is used for identifying the exact faulty line. Several case studies have been used to verify the accuracy of the method. Furthermore, the results of RBFNN and the conventional Multi Layer Perception Neural Network (MLPNN) are also compared. The results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.

Original languageEnglish
Pages (from-to)3032-3041
Number of pages10
JournalJournal of Applied Sciences
Volume10
Issue number23
DOIs
Publication statusPublished - 2010
Externally publishedYes

Keywords

  • Distributed generation (DG)
  • Fault location
  • Perception neural network (MLPNN)
  • Power system
  • Radial basis function neural network (RBFNN)

ASJC Scopus subject areas

  • General

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