A neural network approach to evaluate distribution system reliability

Author

Chen, Jiann-Liang ; Chang, Shao-Hung

Author_Institution

Comput. & Commun. Res. Lab., Ind. Technol. Res. Inst., Taiwan

fYear

1992

fDate

17-19 Sep 1992

Firstpage

487

Lastpage

490

Abstract

An artificial neural network (ANN) approach is presented for evaluating the reliability of distribution systems. A three-layer feedforward network with the backpropagation learning rule is constructed. The developed ANN is used to predict the distribution system reliability from the historic data. The system average interruption frequency index (SAIFI) and the system average interruption duration index (SAIDI) of a real distribution system are computed and compared with results generated by the network method. It was found that the deviation of the results computed by the proposed approach is below 1% and the required running time on a SUN network environment is less than 2 s. Handling the distribution system configuration changes induced by overloading or faults, the ANN approach demonstrates an advantage over the network method

Keywords

backpropagation; distribution networks; feedforward neural nets; power engineering computing; power system reliability; SUN network environment; backpropagation learning rule; distribution systems; neural network; power engineering computing; reliability; system average interruption duration index; system average interruption frequency index; three-layer feedforward network; Artificial neural networks; Computer networks; Distributed computing; Frequency; Iterative algorithms; Logic; Neural networks; Paper technology; Reliability; State-space methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems Engineering, 1992., IEEE International Conference on

Conference_Location

Kobe

Print_ISBN

0-7803-0734-8

Type

conf

DOI

10.1109/ICSYSE.1992.236983

Filename

236983