TY - GEN
T1 - Determining exact fault location in a distribution network in presence of DGs using RBF neural networks
AU - Zayandehroodi, Hadi
AU - Mohamed, Azah
AU - Shareef, Hussain
AU - Mohammadjafari, Marjan
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Distributed Generation (DG)
KW - Distribution Network
KW - Fault Location
KW - Protection
KW - Radial Basis Function Neural Network (RBFNN)
UR - http://www.scopus.com/inward/record.url?scp=80053168527&partnerID=8YFLogxK
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U2 - 10.1109/IRI.2011.6009587
DO - 10.1109/IRI.2011.6009587
M3 - Conference contribution
AN - SCOPUS:80053168527
SN - 9781457709661
T3 - Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011
SP - 434
EP - 438
BT - Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011
T2 - 12th IEEE International Conference on Information Reuse and Integration, IRI 2011
Y2 - 3 August 2011 through 5 August 2011
ER -