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Название: Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate
Авторы: Senyuk, M.
Beryozkina, S.
Gubin, P.
Dmitrieva, A.
Kamalov, F.
Safaraliev, M.
Zicmane, I.
Дата публикации: 2022
Издатель: MDPI
Библиографическое описание: Senyuk, M, Beryozkina, S, Gubin, P, Dmitrieva, A, Kamalov, F, Safaraliev, M & Zicmane, I 2022, 'Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate', Mathematics, Том. 10, № 21, 3949. https://doi.org/10.3390/math10213949
Senyuk, M., Beryozkina, S., Gubin, P., Dmitrieva, A., Kamalov, F., Safaraliev, M., & Zicmane, I. (2022). Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate. Mathematics, 10(21), [3949]. https://doi.org/10.3390/math10213949
Аннотация: The study examines the development and testing of algorithms for disturbance inception time estimation in a power system using instantaneous values of current and voltage with a high sampling rate. The algorithms were tested on both modeled and physical data. The error of signal extremum forecast, the error of signal form forecast, and the signal value at the so-called joint point provided the basis for the suggested algorithms. The method of tuning for each algorithm was described. The time delay and accuracy of the algorithms were evaluated with varying tuning parameters. The algorithms were tested on the two-machine model of a power system in Matlab/Simulink. Signals from emergency event recorders installed on real power facilities were used in testing procedures. The results of this study indicated a possible and promising application of the suggested methods in the emergency control of power systems. © 2022 by the authors.
Ключевые слова: APPROXIMATION
DIGITAL SIGNAL PROCESSING
MATHEMATICAL MODELING
POWER SYSTEM
STATISTICAL ANALYSIS
TIME-SERIES ANALYSIS
URI: http://elar.urfu.ru/handle/10995/131292
Условия доступа: info:eu-repo/semantics/openAccess
cc-by
Текст лицензии: https://creativecommons.org/licenses/by/4.0/
Идентификатор SCOPUS: 85141839006
Идентификатор WOS: 000882323300001
Идентификатор PURE: 31733057
df97cb00-8780-47c3-a177-9260bf3771c2
ISSN: 2227-7390
DOI: 10.3390/math10213949
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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