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Determining exact fault location in a distribution network in presence of DGs using RBF neural networks
IEEE International Conference on Information Reuse and Integration (IRI) (2011)
  • Hadi Zayandehroodi
  • Azah Mohamed
  • Hussain Shareef
  • Marjan Mohammadjafari
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.
Keywords
  • Fault Location; Protection; Distributed Generation (DG); Distribution Network; Radial Basis Function Neural Network (RBFNN)
Disciplines
Publication Date
Summer September 6, 2011
Citation Information
Hadi Zayandehroodi, Azah Mohamed, Hussain Shareef and Marjan Mohammadjafari. "Determining exact fault location in a distribution network in presence of DGs using RBF neural networks" IEEE International Conference on Information Reuse and Integration (IRI) (2011)
Available at: http://works.bepress.com/hadi_zayandehroodi/12/