The purpose of this paper is to improve the online monitoring level of low-frequency oscillation in the power system. A modal identification method of discrete Fourier transform (DFT) curve fitting based on ambient data is proposed in this study.
An autoregressive moving average mathematical model of ambient data was established, parameters of low-frequency oscillation were designed and parameters of low-frequency oscillation were estimated via DFT curve fitting. The variational modal decomposition method is used to filter direct current components in ambient data signals to improve the accuracy of identification. Simulation phasor measurement unit data and measured data of the power grid proved the correctness of this method.
Compared with the modified extended Yule-Walker method, the proposed approach demonstrates the advantages of fast calculation speed and high accuracy.
Modal identification method of low-frequency oscillation based on ambient data demonstrated high precision and short running time for small interference patterns. This study provides a new research idea for low-frequency oscillation analysis and early warning of power systems.
Yan, H.-Y. and Hwang, J.K. (2021), "Mode identification method of low-frequency oscillation for power system based on ambient data", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 40 No. 3, pp. 441-455. https://doi.org/10.1108/COMPEL-10-2020-0337
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