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

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.
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approximation, digital signal processing, mathematical modeling, power system, statistical analysis, time-series analysis
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) doi:10.3390/math10213949