Reliability Evaluation of Distribution Power Systems Based on Artificial Neural Network TechniquesReport as inadecuate




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Journal of Electrical and Computer EngineeringVolume 2012 2012, Article ID 560541, 5 pages

Research Article

Faculty of Electrical and Electronics Engineering, University of Malaysia, Pahang, Lebuhraya Tun Razak, Kuantan Pahang, 26300 Gambang, Malaysia

National Company, Engineering, Transmission Division TNB, Kuala Lumpur, Malaysia

Received 5 May 2011; Revised 21 July 2011; Accepted 25 August 2011

Academic Editor: Edgar Carreno

Copyright © 2012 Mohammoud M. Hadow et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In order to assess the reliability of distribution systems, more and more researchers are directing their attention to the artificial intelligent method, and several reliability indices have been proposed, such as basic load point indices and system performance indices. Artificial neural network is recently established as a useful and much promising too, applied to variety of power systems engineering. This paper presents ANN version for evaluating the reliability of distribution power systems DPSs, in the proposed algorithm, the ANN used to predicted RPS using historical data method constructed according to the backpropagation learning rule. At the same time, System indices such as SAIFI and SAIDI of real distribution system are computed and compared with results generated by network method. The result obtained by proposed method gives acceptable reliability indices and can also found that the deviation of computed values by the proposed method is less than 1% and needs running time on ASUN network environment of less than 2 s. The ANN approach demonstrates advantage over the network method.





Author: Mohammoud M. Hadow, Ahmed N. Abd Allah, and Sazali P. Abdul karim

Source: https://www.hindawi.com/



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