An automated protection method for distribution networks with distributed generations using radial basis function neural network

Hadi Zayandehroodi, Azah Mohamed, Hussain Shareef, Marjan Mohammadjafari

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

5 Citations (Scopus)

Abstract

When distributed generation (DG) penetrates into a distribution system, it will have unfavorable impact on the traditional protection methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This paper presents a new automated protection method using radial basis function neural network (RBFNN) for a distribution system with high penetration DG units. In the proposed method, for implementing fault location considering various types of faults, three staged RBFNNs have been developed. The first RBFNN is used for determining the fault distance from each power source and the second RBFNN is used for identifying the faulty line. To isolate the fault, the third RBFNN has been developed for determining which circuit breakers (CBs) that must open or close. The proposed protection scheme is implemented on a practical test distribution network.

Original languageEnglish
Title of host publication2011 5th International Power Engineering and Optimization Conference, PEOCO 2011 - Program and Abstracts
Pages255-260
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 5th International Power Engineering and Optimization Conference, PEOCO 2011 - Shah Alam, Selangor, Malaysia
Duration: Jun 6 2011Jun 7 2011

Publication series

Name2011 5th International Power Engineering and Optimization Conference, PEOCO 2011 - Program and Abstracts

Conference

Conference2011 5th International Power Engineering and Optimization Conference, PEOCO 2011
Country/TerritoryMalaysia
CityShah Alam, Selangor
Period6/6/116/7/11

Keywords

  • Distributed Generation
  • Distribution Network
  • Fault Location
  • Protection
  • Radial Basis Function Neural Network (RBFNN)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology

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