G. I. Sharaievskyi
Institute for Safety Problems of Nuclear Power Plants, NAS of Ukraine, 12, Lysohirska St., Kyiv, 03028, Ukraine
https://orcid.org/0000-0003-3290-6651
DOI: doi.org/10.31717/2311-8253.26.1.4
Abstract
In this work, the state of existing automated systems for the management of technological processes at nuclear power plants was analyzed, and use of neurocomputing approach to recognition of spectral implementations of stochastic diagnostic signals was proved, which defines actual technical condition of certain NPP equipment. As it is known, the essence of the problem lies in the fact that modern NPP monitoring and control systems, which are the part of computer-aided manufacturing control systems (CAMCS), are based on a deterministic approach to the logical analysis of equipment operating conditions to prevent controlled parameters from exceeding predefined safe limits. It is obviously not enough for reliable prevention of damage that may occur in the main equipment of nuclear power units. In fact, some NPP parameters that are not directly controlled by CAMCS, particularly temperature conditions of reactor heat-exchange devices, may significantly exceed permissible values during operation that can lead to severe damage, for example, heat-exchange crisis causing burnout that may spread throughout the entire nuclear core. To prevent such catastrophic events, it would be necessary to measure local temperature of fuel elements (in reactor about 40,000 practically uncontrolled fuel elements are located). This would require hundreds of thousands of thermocouples on their surfaces, which is technologically impossible. Therefore, the problem of diagnosing abnormal heat-exchange regimes can be solved not by direct temperature measurements of the coolant at the nuclear core inlet as it is now, but by recognizing the spectral characteristics of dynamic components of certain reactor parameters. These include, first of all, signals of dynamic coolant pressure and signals from neutron flux detectors located directly within the reactor core. In this work the approach to building SOM-neural networks regarding the tasks of accidental objects recognition is reviewed. Modified learning algorithm for the SOM-neural structure recognition is proposed in condition of no a priori information on the number of classes to be recognized. This approach is implemented based on the determination of disorder moment in random time series using the autoregressive model.
Keywords: Artificial Neural Networks, self-organizing map, learning algorithm of Kohonen, stochastic dynamical systems, self-adaptive, automatic diagnosis, equipment of NPP.
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