Hybrid identification of unlabeled nuclear power plant transients with artificial neural networks

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 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 correctly classifies unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements, numerical integration accumulating errors, and the digitization of simulated and actual plant signals became obvious. Various ANN based models were successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear power plant simulator.