Modal Parameter Identification of Low Frequency Oscillation in Power System Based on Ambient Data

Abstract

Low frequency oscillation is one of the key problem affecting the safe and stable operation of interconnected power system, variational modal decomposition (VMD) was used to extract low-frequency oscillation signals from ambient data, and a method of low-frequency oscillation modal identification for power systems discrete Fourier transform (DFT)-based curve fitting was proposed in this paper. Firstly, the DC component of ambient data signals was filtered by VMD decomposition to extract low-frequency oscillation signals. The number of VMD decomposition was determined by modal correlation coefficient, which improved the timeliness of signal decomposition. Secondly, the auto regressive moving average (ARMA) model of ambient data was established to simulate the generation of data signals. The DFT curve fitting of low-frequency oscillation signal autocorrelation function was used to estimate the Laplace transform coefficient and extract characteristic parameters of electromechanical oscillation. Finally, Simulation data and some measured phasor measurement unit (PMU) data are used to verify the feasibility and effectiveness of the method. The experiment shows that the sampling VMD algorithm and the curve fitting method based on DFT can extract the characteristic parameters of low-frequency oscillation, which effectively improves the real-time performance of electromechanical small interference stability.

Keywords

• low frequency oscillation
• ambient data
• auto regressive moving average (arma) model
• variational modal decomposition (vmd)
• discrete fourier transform (dft) curve-fitting