Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
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Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements
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| Автор |
Senyuk, M.
Safaraliev, M. Pazderin, A. Pichugova, O. Zicmane, I. Beryozkina, S. |
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| Тематика |
BIG DATA
EMERGENCY CONTROL MACHINE LEARNING PHASOR MEASUREMENT UNITS POWER SYSTEM SMALL-SIGNAL STABILITY SYNCHRONOUS GENERATOR TRANSIENT STABILITY |
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| Описание |
Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems. © 2023 by the authors.
Russian Science Foundation, RSF: 23-79-01024 The reported study was supported by Russian Science Foundation, research project № 23-79-01024. |
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| Дата |
2024-04-05T16:36:57Z
2024-04-05T16:36:57Z 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, Pazderin, A, Pichugova, O, Zicmane, I & Beryozkina, S 2023, 'Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements', Mathematics, Том. 11, № 22, стр. 4667. https://doi.org/10.3390/math11224667
Senyuk, M., Safaraliev, M., Pazderin, A., Pichugova, O., Zicmane, I., & Beryozkina, S. (2023). Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements. Mathematics, 11(22), 4667. https://doi.org/10.3390/math11224667 2227-7390 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178107420&doi=10.3390%2fmath11224667&partnerID=40&md5=8bc73475b69b4db33432cb78a9114ce2 https://www.mdpi.com/2227-7390/11/22/4667/pdf?version=1700134491 http://elar.urfu.ru/handle/10995/130999 10.3390/math11224667 85178107420 001118102500001 |
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| Язык |
en
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| Связанные ресурсы |
info:eu-repo/grantAgreement/RSF//23-79-01024
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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 |
Multidisciplinary Digital Publishing Institute (MDPI)
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| Источник |
Mathematics
Mathematics |
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