TY - GEN
T1 - Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs
AU - Zayandehroodi, Hadi
AU - Mohamed, Azah
AU - Shareef, Hussain
AU - Mohammadjafari, Marjan
PY - 2010
Y1 - 2010
N2 - With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.
AB - With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN 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 - Multilayer perceptron neural network (MLPNN)
KW - Radial basis function neural network (RBFNN)
UR - http://www.scopus.com/inward/record.url?scp=79951801980&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951801980&partnerID=8YFLogxK
U2 - 10.1109/PECON.2010.5699422
DO - 10.1109/PECON.2010.5699422
M3 - Conference contribution
AN - SCOPUS:79951801980
SN - 9781424489466
T3 - PECon2010 - 2010 IEEE International Conference on Power and Energy
SP - 341
EP - 345
BT - PECon2010 - 2010 IEEE International Conference on Power and Energy
T2 - 2010 IEEE International Conference on Power and Energy, PECon2010
Y2 - 29 November 2010 through 1 December 2010
ER -