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Automated Fault Location in a Power System with Distributed Generations Using Radial Basis Function Neural Networks
Journal of Applied Sciences (2010)
  • Hadi Zayandehroodi
  • Azah Mohamed
  • Hussain Shareef
  • Marjan Mohammadjafari
Abstract
High penetration of Distributed Generation (DG) units will have unfavorable impacts on the traditional fault location methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This study 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 determined first by normalizing the fault currents of the main source. Then to determine the fault location, two RBFNNs have been developed for various fault types. The first RBFNN is used for detraining fault distance from each source and the second RBFNN is used for identifying the exact faulty line. Several case studies have been used to verify the accuracy of the method. Furthermore, the results of RBFNN and the conventional Multi Layer Perception Neural Network (MLPNN) are also compared. The results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.
Keywords
  • Fault location,
  • distributed generation (DG),
  • power system,
  • radial basis function neural network (RBFNN),
  • perception neural network (MLPNN)
Disciplines
Publication Date
2010
Citation Information
Hadi Zayandehroodi, Azah Mohamed, Hussain Shareef and Marjan Mohammadjafari. "Automated Fault Location in a Power System with Distributed Generations Using Radial Basis Function Neural Networks" Journal of Applied Sciences Vol. 10 Iss. 23 (2010)
Available at: http://works.bepress.com/azah_mohamed/31/