Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
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| Автор |
Senyuk, M.
Safaraliev, M. Kamalov, F. Sulieman, H. |
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| Тематика |
ENSEMBLE MACHINE LEARNING
EXTREME GRADIENT BOOSTING POWER SYSTEM MODELING RANDOM FOREST TRANSIENT STABILITY |
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| Описание |
This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic stability assessment is achieved. The selected research topic relates to the issue of changing the structure and parameters of modern power systems. The key features of modern power systems include the following: decreased total inertia caused by integration of renewable sources energy, stricter requirements for emergency control accuracy, highly digitized operation and control of power systems, and high volumes of data that describe power system operation. Arranging emergency control in these new conditions is one of the prominent problems in modern power systems. In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. Transient stability of a power system was analyzed as the base function. Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. In the case of the test sample, accuracy of instability classification for XGBoost was 91.5%, while that for Random Forest was 81.6%. The accuracy of algorithms increased by 10.9% and 1.5%, respectively, when the topology of the power system was taken into account. © 2023 by the authors.
American University of Sharjah, AUS This research was supported, in part, by the Open Access Program from the American University of Sharjah. |
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| Дата |
2024-04-05T16:15:21Z
2024-04-05T16:15:21Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Senyuk, M, Safaraliev, M, Kamalov, F & Sulieman, H 2023, 'Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology', Mathematics, Том. 11, № 3, 525. https://doi.org/10.3390/math11030525
Senyuk, M., Safaraliev, M., Kamalov, F., & Sulieman, H. (2023). Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology. Mathematics, 11(3), [525]. https://doi.org/10.3390/math11030525 2227-7390 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147885714&doi=10.3390%2fmath11030525&partnerID=40&md5=98124c34d760a201277038db6d691ef3 https://www.mdpi.com/2227-7390/11/3/525/pdf?version=1674051917 http://elar.urfu.ru/handle/10995/130194 10.3390/math11030525 85147885714 000931020800001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
MDPI
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| Источник |
Mathematics
Mathematics |
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