Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs

Hadi Zayandehroodi, Azah Mohamed, Hussain Shareef, Marjan Mohammadjafari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper 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 first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.

Original languageEnglish
Title of host publicationPECon2010 - 2010 IEEE International Conference on Power and Energy
Pages341-345
Number of pages5
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Power and Energy, PECon2010 - Kuala Lumpur, Malaysia
Duration: Nov 29 2010Dec 1 2010

Publication series

NamePECon2010 - 2010 IEEE International Conference on Power and Energy

Conference

Conference2010 IEEE International Conference on Power and Energy, PECon2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period11/29/1012/1/10

Keywords

  • Distributed generation (DG)
  • Distribution network
  • Fault location
  • Multilayer perceptron neural network (MLPNN)
  • Radial basis function neural network (RBFNN)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology

Fingerprint

Dive into the research topics of 'Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs'. Together they form a unique fingerprint.

Cite this