DocumentCode :
3154768
Title :
Determining exact fault location in a distribution network in presence of DGs using RBF neural networks
Author :
Zayandehroodi, Hadi ; Mohamed, Azah ; Shareef, Hussain ; Mohammadjafari, Marjan
Author_Institution :
Dept. of Electr., Electron. & Syst. Eng., Univ. Kebangsaan Malaysia (UKM), Bangi, Malaysia
fYear :
2011
fDate :
3-5 Aug. 2011
Firstpage :
434
Lastpage :
438
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 :
distributed power generation; mathematics computing; power distribution protection; power engineering computing; radial basis function networks; DIgSILENT Power Factory 14.0.523; MATLAB based developed software; RBF neural networks; automated fault location method; distributed generators; distribution network; distribution system; fault diagnosis; network protection devices; power system; radial basis function neural network; Circuit faults; Fault currents; Fault location; Neurons; Testing; Training; Distributed Generation (DG); Distribution Network; Fault Location; Protection; Radial Basis Function Neural Network (RBFNN);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Reuse and Integration (IRI), 2011 IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4577-0964-7
Electronic_ISBN :
978-1-4577-0965-4
Type :
conf
DOI :
10.1109/IRI.2011.6009587
Filename :
6009587
Link To Document :
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