EPJ Web Conf.
Volume 247, 2021PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|Number of page(s)||8|
|Published online||22 February 2021|
INTELLIGENT TECHNIQUES FOR ANOMALY DETECTION IN NUCLEAR REACTORS
Institute of Communication and Computer Systems, National Technical University of Athens, Greece
Published online: 22 February 2021
The safe operation of nuclear power plants is highly dependent on the ability of quickly and accurately identifying possible anomalies and perturbations in the reactor. Operational defects are primarily diagnosed by detectors that capture changes in the neutron flux, placed at various points inside and outside of the core. Neutron flux signals are subsequently analyzed with signal processing techniques in an effort to be better described (have their higher-order characteristics uncovered, locate transient events, etc). To this end, the application of intelligent techniques may be extremely beneficial, as it may assist and extend the current level of analysis. Besides, the combination of signal processing methodologies and machine learning techniques in the framework of nuclear power plant data is an emerging topic that has yet to show its full potential. In this context, the current contribution attempts at introducing intelligent approaches and more specifically, deep learning techniques, in neutron flux signal analysis for the identification of perturbations and other anomalies in the reactor core that may affect its operational capabilities. The obtained results of an initial stage of analysis on neutron flux signals captured at pressurized water reactors are encouraging, underlying the robustness and the potential of the proposed approach.
Key words: Anomaly Detection / Nuclear Power Plants / Deep Neural Networks / Multi-class / Classification
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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