Title :
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
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;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.626219
