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Journal of Applied Sciences
  Year: 2010 | Volume: 10 | Issue: 23 | Page No.: 3032-3041
DOI: 10.3923/jas.2010.3032.3041
 
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Automated Fault Location in a Power System with Distributed Generations using Radial basis Function Neural Networks

H. Zayandehroodi, A. Mohamed, H. Shareef and M. 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.
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How to cite this article:

H. Zayandehroodi, A. Mohamed, H. Shareef and M. Mohammadjafari, 2010. Automated Fault Location in a Power System with Distributed Generations using Radial basis Function Neural Networks. Journal of Applied Sciences, 10: 3032-3041.

DOI: 10.3923/jas.2010.3032.3041

URL: https://scialert.net/abstract/?doi=jas.2010.3032.3041

COMMENTS
13 November, 2010
Mahmood Haghifam:
This article is a good starting point for interesting topic and there is a clear contribution potential in this manuscript.
10 December, 2010
Hadi Zayandehroodi:

Dear professor, Thank you for your consideration.

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