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Белоусов, Павел Анатольевич

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Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
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Белоусов
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Павел Анатольевич
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2019 - 20202
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    The emergence of technogenic risks in the event of a heat exchange crisis in nuclear power plants
    (2019) Belousov, P. A.; Raspopov, D. A.; Stepanchenko, K. P.; Белоусов, Павел Анатольевич
    © Published under licence by IOP Publishing Ltd. In this article, the main problems and man-caused risks are considered in the event of a heat exchange crisis in nuclear power plants (NPP). A thermophysical booth simulating the operation of a nuclear reactor was developed. The data of acoustic diagnostics of the boiling of the coolant on the basis of the work of the laboratory stand were collected. Methods for processing and analyzing data collected from acoustic sensors have been developed. The recording and processing of signals was carried out using the application package and MATLAB programming language. The article describes the application of correlation and spectral analysis for data processing and analysis. The authors also use regression analysis to find the dependence of wall temperature on the frequency of acoustic oscillations. Forecasting the values of the wall temperature from the frequency of sound vibrations makes it possible in the future to find diagnostic features and build a mathematical model for detecting boiling and heat transfer crisis in nuclear power plants.
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    Development of methods and algorithms for identification of a type of electric energy consumers using artificial intelligence and machine learning models for Smart Grid Systems
    (2020) Raspopov, D.; Belousov, P.; Белоусов, Павел Анатольевич
    © 2020 The Authors. Published by Elsevier B.V.Article presents the relevance of creating Smart Grid for public power networks and industrial enterprises. The article describes an experiment in which data were collected from current and voltage sensors from several different consumers of electric energy, using the created Smart Socket-Smart Energy device. The methods and algorithms of intellectual and spectral analysis were used to analyze and process experimental data. In this paper, we developed an algorithm for identifying the type of consumer of electric energy in the network of general power supply using the machine learning model XGBoost-extreme gradient boosted decision trees.