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

doi:10.12096/j.2096-4528.pgt.21083

Abstract

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.

Key words: low frequency oscillation, ambient data, auto regressive moving average (ARMA) model, variational modal decomposition (VMD), discrete Fourier transform (DFT) curve-fitting

CLC Number:

TM 76

Hongyan YAN, Jin Kwon HWANG, Yanfeng GAO. Modal Parameter Identification of Low Frequency Oscillation in Power System Based on Ambient Data[J]. Power Generation Technology, 2022, 43(1): 19-31.

Figures/Tables 16

Tab. 1 Parameters of the analog DC component

DC 趋势项	数值
1/K_s(幅值)	0.3
K_sf₀/(2J)	0.2

Tab. 2 Parameters of low-frequency oscillation components

k	f_k /Hz	ζ_k	γ_k
1	0.3	0.07	0.4
2	0.5	0.05	0.5

Tab. 3 Maximum correlation coefficients of K different values

模态个数	2	3	4	5
最大相关系数	0.003 0	0.061 2	0.124 1	0.147 6

Fig. 1 The spectrum and VMD modal component

Fig. 2 The spectrum and EMD modal component

Fig. 3 Frequency deviation waveform and autocorrelation function of IMF1

Fig. 4 DFT amplitude and angle of IMF1

Fig. 5 Frequencies and damping rates identification results of IMF1

Tab. 4 Identification results of simulated data

算法	模态	$f ̂ k$ /Hz	ζ_k	$A ̂ k / 10 - 2$	$φ ̂ k$ /(°)	S_NRe
本文算法 SNR=30 dB	1	0.296 8	0.072 7	0.487 1	78.910 0	12.358 0
本文算法 SNR=30 dB	2	0.500 6	0.051 3	0.261 3	95.591 0	13.455 7
MEYW方法 SNR=30 dB	1	0.304 6	0.061 3	0.430 3	73.776 0	10.548 1
MEYW方法 SNR=30 dB	2	0.504 8	0.058 5	0.181 9	102.673 3	12.909 1
本文算法 SNR=10 dB	1	0.295 4	0.070 8	0.611 7	86.660 2	11.351 7
本文算法 SNR=10 dB	2	0.503 4	0.046 7	0.312 5	99.217 9	12.765 3
MEYW方法 SNR=10 dB	1	0.305 2	0.054 0	0.489 6	82.909 7	10.840 1
MEYW方法 SNR=10 dB	2	0.505 5	0.037 4	0.220 3	97.576 4	11.890 3

Tab. 4 Identification results of simulated data

算法	模态	$f ̂ k$ /Hz	ζ_k	$A ̂ k / 10 - 2$	$φ ̂ k$ /(°)	S_NRe
本文算法 SNR=30 dB	1	0.296 8	0.072 7	0.487 1	78.910 0	12.358 0
本文算法 SNR=30 dB	2	0.500 6	0.051 3	0.261 3	95.591 0	13.455 7
MEYW方法 SNR=30 dB	1	0.304 6	0.061 3	0.430 3	73.776 0	10.548 1
MEYW方法 SNR=30 dB	2	0.504 8	0.058 5	0.181 9	102.673 3	12.909 1
本文算法 SNR=10 dB	1	0.295 4	0.070 8	0.611 7	86.660 2	11.351 7
本文算法 SNR=10 dB	2	0.503 4	0.046 7	0.312 5	99.217 9	12.765 3
MEYW方法 SNR=10 dB	1	0.305 2	0.054 0	0.489 6	82.909 7	10.840 1
MEYW方法 SNR=10 dB	2	0.505 5	0.037 4	0.220 3	97.576 4	11.890 3

Fig. 6 Fitting precision diagram

Fig. 7 The measured frequency fluctuation of a power grid

Fig. 8 Modal components and spectrum of a power network measured frequency data

Tab. 5 Identification results of measured data

算法	模态	$f ̂ k$ /Hz	ζ_k	$A ̂ k / 10 - 6$	$φ ̂ k$ /(°)	S_NRe
本文算法	1	0.319 5	0.049 40	0.885 2	89.987 9	15.626 3
本文算法	2	0.675 3	0.135 1	0.323 8	112.821 0	12.260 1
MEYW法	1	0.319 3	0.063 7	0.982 4	93.850 4	14.963 0
MEYW法	2	0.667 4	0.098 7	0.266 7	113.287 0	11.762 7

Tab. 5 Identification results of measured data

算法	模态	$f ̂ k$ /Hz	ζ_k	$A ̂ k / 10 - 6$	$φ ̂ k$ /(°)	S_NRe
本文算法	1	0.319 5	0.049 40	0.885 2	89.987 9	15.626 3
本文算法	2	0.675 3	0.135 1	0.323 8	112.821 0	12.260 1
MEYW法	1	0.319 3	0.063 7	0.982 4	93.850 4	14.963 0
MEYW法	2	0.667 4	0.098 7	0.266 7	113.287 0	11.762 7

Fig. 9 IMF1 DFT amplitude and angle of measured data

Fig. 10 Two modes identification results of measured data

Fig. 11 Fitting accuracy of measured data

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