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Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements

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Заглавие Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements
 
Автор Senyuk, M.
Safaraliev, M.
Pazderin, A.
Pichugova, O.
Zicmane, I.
Beryozkina, S.
 
Тематика BIG DATA
EMERGENCY CONTROL
MACHINE LEARNING
PHASOR MEASUREMENT UNITS
POWER SYSTEM
SMALL-SIGNAL STABILITY
SYNCHRONOUS GENERATOR
TRANSIENT STABILITY
 
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.
 
Дата 2024-04-05T16:36:57Z
2024-04-05T16:36:57Z
2023
 
Тип Article
Journal article (info:eu-repo/semantics/article)
|info:eu-repo/semantics/publishedVersion
 
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
 
Язык en
 
Связанные ресурсы info:eu-repo/grantAgreement/RSF//23-79-01024
 
Права Open access (info:eu-repo/semantics/openAccess)
cc-by
https://creativecommons.org/licenses/by/4.0/
 
Формат application/pdf
 
Издатель Multidisciplinary Digital Publishing Institute (MDPI)
 
Источник Mathematics
Mathematics