Artificial neural networks and their application to power system industry

Author

Marpaka, D.R. ; Devgan, S.S. ; Bodruzzaman, M. ; Kari, Suresh ; Sharaeh, S. Al

Author_Institution

Dept. of Electr. Eng., Tennessee State Univ., Nashville, TN, USA

fYear

1992

fDate

12-15 Apr 1992

Firstpage

354

Abstract

A method to apply neural-network technology to the study of transient stability of electric power systems is presented. During the training phase the network is presented with a set of input and output data obtained from an offline study. After the network has obtained the ability to compute the desired output, the network is presented with the data representing different operating conditions for critical cleaning time estimation. An attempt is made to present a design philosophy to determine the most effective architecture for this problem

Keywords

neural nets; power system analysis computing; power system stability; power system transients; backpropagation algorithm; critical cleaning time estimation; design philosophy; electric power systems; network training phase; neural-network technology; power system industry; transient stability; Artificial neural networks; Circuit faults; Power system analysis computing; Power system interconnection; Power system reliability; Power system stability; Power system transients; Power systems; Stability analysis; Transient analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Southeastcon '92, Proceedings., IEEE

Conference_Location

Birmingham, AL

Print_ISBN

0-7803-0494-2

Type

conf

DOI

10.1109/SECON.1992.202369

Filename

202369