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Название: Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine
Авторы: Senyuk, M.
Beryozkina, S.
Safaraliev, M.
Nadeem, M.
Odinaev, I.
Kamalov, F.
Дата публикации: 2024
Издатель: Multidisciplinary Digital Publishing Institute (MDPI)
Библиографическое описание: Senyuk, M., Beryozkina, S., Safaraliev, M., Nadeem, M., Odinaev, I., & Kamalov, F. (2024). Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine. Mathematics, 12(11), [1644]. https://doi.org/10.3390/math12111644
Аннотация: This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine’s cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14%. The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research. © 2024 by the authors.
Ключевые слова: FAST VALVING
MACHINE LEARNING
POWER SYSTEM
STEAM TURBINE
SYNCHRONOUS GENERATOR
TRANSIENT STABILITY
URI: http://elar.urfu.ru/handle/10995/141732
Условия доступа: info:eu-repo/semantics/openAccess
cc-by
Идентификатор РИНЦ: 67751142
Идентификатор SCOPUS: 85195921830
Идентификатор WOS: 001245709600001
Идентификатор PURE: 58566149
ISSN: 2227-7390
DOI: 10.3390/math12111644
Сведения о поддержке: Russian Science Foundation, RSF, (23-79-01024)
The reported study was supported by the Russian Science Foundation, research project No. 23-79-01024.
Карточка проекта РНФ: 23-79-01024)
The reported study was supported by the Russian Science Foundation, research project No. 23-79-01024.
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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