Identification of nuclear power plant transients with neural networks

Author

Embrechts, Mark J. ; Benedek, Sandor

Author_Institution

Dept. of Decision Sci. & Eng. Syst., Rensselaer Polytech. Inst., Troy, NY, USA

Volume

1

fYear

1997

fDate

12-15 Oct 1997

Firstpage

912

Abstract

Rapid identification of malfunctions is of premier importance for the safe operation of nuclear power plants. In order to provide sufficient lead time, malfunctions have to be identified within 60 seconds. A feedforward neural network trained with the backpropagation algorithm was developed to model simulated nuclear power plant malfunctions for a pressurized water reactor (PWR) and this model was then successfully applied to identify malfunctions of the Hungarian Paks nuclear power plant simulator

Keywords

backpropagation; fault location; feedforward neural nets; fission reactor safety; identification; nuclear engineering computing; nuclear power stations; power plants; power system transients; Hungarian Paks nuclear power plant simulator; PWR; backpropagation algorithm; feedforward neural network; lead time; neural networks; nuclear power plant transient identification; pressurized water reactor; rapid identification; safe operation; simulated nuclear power plant malfunctions; Aggregates; Artificial neural networks; Backpropagation algorithms; Feedforward neural networks; Inductors; Neural networks; Nuclear power generation; Power engineering and energy; Power generation; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.626219

Filename

626219