Tashchilin V.A., Gubin P.Yu., Shakirov M.M. Review of Machine Learning Based on PMU Application in Terms of Power System Emergency Control

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Abstract

Wide penetration of phasor measurement systems in electrical power industry provides new opportunities in terms of bulk power systems control as well as distribution and microgrids. Possible applications of PMU are not limited to the state estimation problem and cover problems of transient conditions analysis. This fact lays the basis for further development of remedial action schemes, which use not only local measurements from an installation point, but also global ones, covering a part of a system or even the system totally. This review observes the existing approaches to integration of PMU to the following emergency control schemes and problems: automatic frequency load shedding, out-of-step conditions liquidation automation, islanding control, controlled emergency islanding automation, state and stability control schemes, identification and damping of electromechanical oscillations, identification, failure state classification and system conditions. It is shown that significant attention is being paid to different machine learning algorithms. There are two primary barriers to further development of remedial action schemes based on PMU. Firstly, it is assumed that there is information redundancy from measurement units. This does not completely correspond to the existing situation, because the number of installed units is still relatively small. Secondly, widely proposed machine learning algorithms still find very limited or almost no application in power system control because of general subjective attitude and difficulties connected with training process and lack of real PMU data for testing algorithms proposed. In particular, as it is shown in this work, real PMU data was used to implement and analyze the effectiveness of identification and classification of failure states procedure only.

Keywords

phasor measurement system, emergency control, automatic frequency load shedding, out-of-step conditions, liquidation automation, stability control scheme, machine learning, artificial neural networks.

Valeriy A. Tashchilin Ph.D. (Engineering), Leading Engineer, Associate Professor, Department of Automated Electrical Systems, Ural Power Engineering Institute, Ural Federal University, Yekaterinburg, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0001-8763-3705

Pavel Yu. Gubin Postgraduate Student, Teaching Assistant, Department of Automated Electrical Systems, Ural Power Engineering Institute, Ural Federal University, Yekaterinburg, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0002-3736-652X

Mikhail M. Shakirov Master’s Degree Student, Department of Automated Electrical Systems, Ural Power Engineering Institute, Ural Federal University, Yekaterinburg, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0002-8192-8112

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Tashchilin V.A., Gubin P.Yu., Shakirov M.M. Review of Machine Learning Based on PMU Application in Terms of Power System Emergency Control. Elektrotekhnicheskie sistemy i kompleksy [Electrotechnical Systems and Complexes], 2022, no. 3(56), pp. 12-27. (In Russian). https://doi.org/10.18503/2311-8318-2022-3(56)-12-27

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