Abstract
Purpose
Electromechanical oscillations between interconnected generators are considered a major threat to the secure operation of power systems. Therefore, oscillation monitoring systems in real-time are of critical importance to detect the danger of poorly damped oscillations. For the detection and analysis of the oscillations, high-temporal-resolution measurements are required according to the Nyquist theorem. This paper proposes a novel algorithm for the identification of electromechanical oscillations using low-sampled data such as supervisory control and data acquisition (SCADA) measurements.
Methods
The lack of temporal resolution of the data is compensated by using low-sampled data sets at multiple different locations. At a target location, a high-sampled data-signal can be reconstructed using mode shape information obtained from model-based modal analysis. The variable projection method is then used to detect oscillations and estimate oscillation components including frequency and damping ratio.
Results
Case studies based on practical Korean power systems are presented to evaluate the performance of the proposed method. Simulation results show that the proposed method can detect and identify electromechanical oscillations with low-sampled data.
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant number 2018R1D1A1B07043818).
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Baek, JO., Kim, S. Detection and Analysis of Electromechanical Oscillation in Power Systems with Low-Sampled Data Using Modal Analysis Methods. J. Electr. Eng. Technol. 15, 1999–2006 (2020). https://doi.org/10.1007/s42835-020-00471-0
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DOI: https://doi.org/10.1007/s42835-020-00471-0