Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

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dc.contributor.authorSenyuk, Mihail
dc.contributor.authorBeryozkina, Svetlana
dc.contributor.authorSafaraliev, Murodbek
dc.contributor.authorPazderin, Andrey
dc.contributor.authorOdinaev, Ismoil
dc.contributor.authorKlassen, Viktor
dc.contributor.authorSavosina, Alena
dc.contributor.authorKamalov, Firuz
dc.date.accessioned2024-04-22T16:26:47Z
dc.date.available2024-04-22T16:26:47Z
dc.date.copyright© 2024
dc.date.issued2024-02
dc.description.abstractModern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. © 2024 by the authors.
dc.identifier.citationSenyuk, M., Beryozkina, S., Safaraliev, M., Pazderin, A., Odinaev, I., Klassen, V., Savosina, A., & Kamalov, F. (2024). Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review. Energies, 17(4), 764. https://doi.org/10.3390/en17040764
dc.identifier.issn19961073
dc.identifier.urihttps://doi.org/10.3390/en17040764
dc.identifier.urihttps://hdl.handle.net/20.500.12519/1028
dc.language.isoen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relationAuthors Affiliations : Senyuk, M., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Beryozkina, S., College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; Safaraliev, M., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Pazderin, A., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Odinaev, I., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Klassen, V., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Savosina, A., Department of Electric Drive and Automation of Industrial Installations, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, 117781, United Arab Emirates
dc.relation.ispartofseriesEnergies; Volume 17, Issue 4
dc.rightsCreative Commons Attribution (CC BY) license
dc.rights.holderCopyright : © 2024 by the authors.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectbig data
dc.subjectbulk power system
dc.subjectcontrol action
dc.subjectdigital signal processing
dc.subjectemergency control
dc.subjectmachine learning
dc.subjectphasor measurement units
dc.subjectpower system
dc.subjectsmall signal stability
dc.subjectsynchronous generator
dc.subjecttransient stability
dc.subjectwide area protection system
dc.titleBulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review
dc.typeArticle

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