Hybrid identification of nuclear power plant transients with artificial neural networks
Embrechts, M.J.
Benedek, S.
Dept. of Decision Sci. & Eng. Syst., Rensselaer Polytech. Inst., Troy, NY, USA;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: June 2004
Volume: 51,
Issue: 3
On page(s): 686- 693
ISSN: 0278-0046
INSPEC Accession Number: 8110762
Digital Object Identifier: 10.1109/TIE.2004.824874
Current Version Published: 2004-06-01
Abstract
Proper and rapid identification of malfunctions (transients) is of premier importance for the safe operation of nuclear power plants. Feedforward neural networks trained with the backpropagation (BP) algorithm are frequently applied to model simulated nuclear power plant malfunctions. The correct identification of unlabeled transients-or transients of the "don't-know" type have proven to be especially challenging. A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements of a simulator, the digitization of simulated and actual plant signals, and the accumulating errors during numerical integration became obvious. Beside the feedforward neural networks trained with the BP algorithm, many other types of networks and codes were used for finding the best (sensitive and robust) algorithms. Various neural network based models were successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear power plant simulator. The BP and probabilistic methods have been proven as the most robust against the misleading recognition of unlabeled malfunctions.
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