Determining exact fault location in a distribution network in presence of DGs using RBF neural networks

Hadi Zayandehroodi, Azah Mohamed, Hussain Shareef, Marjan Mohammadjafari

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

17 Citations (Scopus)

Abstract

The increase in interconnection of distributed generators (DGs) to distribution network will greatly affect the configuration and operation mode of the power system, especially with respect to the protection scheme. However, when DG units are connected to a distribution network, the system is no longer radial, which causes a loss of coordination among network protection devices and will have unfavorable impacts on the traditional fault location methods. In this paper a new automated fault location method by using radial basis function neural network (RBFNN) for a distribution network with DGs has presented. The suggested approach is able to determine the accurate type and location of faults using RBF neural network. Several case studies have been made to verify the accuracy of the proposed method for fault diagnosis in a distribution system with DGs using a MATLAB based developed software and DIgSILENT Power Factory 14.0.523. Results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.

Original languageEnglish
Title of host publicationProceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011
Pages434-438
Number of pages5
DOIs
Publication statusPublished - 2011
Event12th IEEE International Conference on Information Reuse and Integration, IRI 2011 - Las Vegas, NV
Duration: 3 Aug 20115 Aug 2011

Other

Other12th IEEE International Conference on Information Reuse and Integration, IRI 2011
CityLas Vegas, NV
Period3/8/115/8/11

Fingerprint

Electric fault location
Electric power distribution
Neural networks
MATLAB
Failure analysis
Industrial plants
Distribution network
RBF neural network
Generator
Fault

Keywords

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

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management

Cite this

Zayandehroodi, H., Mohamed, A., Shareef, H., & Mohammadjafari, M. (2011). Determining exact fault location in a distribution network in presence of DGs using RBF neural networks. In Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011 (pp. 434-438). [6009587] https://doi.org/10.1109/IRI.2011.6009587

Determining exact fault location in a distribution network in presence of DGs using RBF neural networks. / Zayandehroodi, Hadi; Mohamed, Azah; Shareef, Hussain; Mohammadjafari, Marjan.

Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011. 2011. p. 434-438 6009587.

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

Zayandehroodi, H, Mohamed, A, Shareef, H & Mohammadjafari, M 2011, Determining exact fault location in a distribution network in presence of DGs using RBF neural networks. in Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011., 6009587, pp. 434-438, 12th IEEE International Conference on Information Reuse and Integration, IRI 2011, Las Vegas, NV, 3/8/11. https://doi.org/10.1109/IRI.2011.6009587
Zayandehroodi H, Mohamed A, Shareef H, Mohammadjafari M. Determining exact fault location in a distribution network in presence of DGs using RBF neural networks. In Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011. 2011. p. 434-438. 6009587 https://doi.org/10.1109/IRI.2011.6009587
Zayandehroodi, Hadi ; Mohamed, Azah ; Shareef, Hussain ; Mohammadjafari, Marjan. / Determining exact fault location in a distribution network in presence of DGs using RBF neural networks. Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011. 2011. pp. 434-438
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