Search - Issue 30 - USE OF NEURAL NETWORK TECHNOLOGIES TO RECOVER FAILED DETECTOR SIGNAL

USE OF NEURAL NETWORK TECHNOLOGIES TO RECOVER FAILED DETECTOR SIGNAL
DOI: https://doi.org/10.31717/1813-3584.18.30.2
Cover not present USE OF NEURAL NETWORK TECHNOLOGIES TO RECOVER FAILED DETECTOR SIGNAL
Category: Issue 30
Publication: 30
Summary

The paper presents the results of research on the restoration of signals of failed self-powered neutron detector (SPND) based on the application of neural network technologies. The general information on the development, improvement and importance of determining the linear energy release of the fuel rod in the VVER in-core monitoring system is presented. L-BFGS algorithm and the modified Levenberg - Marquardt method were used for the training of the neural network. These algorithms were used to study the neural network based on the in-core monitoring system data from the power units of ZNPP-5, KhNPP-1 and KhNPP-2. Self-powered neutron detectors with different degree of burn-up and in different core positions were chosen for simulation. The analysis of simulation data for selected SPNDs has shown that failed detector signal recovery is possible even at a short training set of the neural network with an error not more than 2 %.

Keywords: fuel rod linear energy release, self-powered neutron detector, neutron flux measuring channel, incore monitoring system, neural network.

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