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

Mihail Senyuk, Svetlana Beryozkina (), Pavel Gubin, Anna Dmitrieva, Firuz Kamalov, Murodbek Safaraliev and Inga Zicmane
Additional contact information
Mihail Senyuk: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Svetlana Beryozkina: College of Engineering and Technology, American University of the Middle East, Kuwait
Pavel Gubin: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Anna Dmitrieva: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Firuz Kamalov: Department of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab Emirates
Murodbek Safaraliev: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Inga Zicmane: Faculty of Electrical and Environmental Engineering, Riga Technical University, LV-1048 Riga, Latvia

Mathematics, 2022, vol. 10, issue 21, 1-19

Abstract: 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.

Keywords: approximation; digital signal processing; mathematical modeling; power system; statistical analysis; time-series analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/21/3949/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/21/3949/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:21:p:3949-:d:951937

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().