Abstract
To more completely extract useful features from low frequency oscillation (LFO) signals, a time-frequency analysis method using resonance-based sparse signal decomposition (RSSD) and a frequency slice wavelet transform (FSWT) is proposed. FSWT can cut time-frequency areas freely, so that any band component feature can be extracted. It can analyze multiple aspects of the LFO signal, including determination of dominant mode, mode seperation and extraction, and 3D map expression. Combined with the Hilbert transform,the parameters of the LFO mode components can be identified. Furthermore, the noise in the LFO signal could reduce the frequency resolution of FSWT analysis, which may impact the accuracy of oscillation mode identification. Complex signals can be separated by predictable Q-factors using RSSD. The RSSD method can do well in LFO signal denoising. Firstly, the LFO signal is decomposed into a high-resonance component, a low-resonance component and a residual by RSSD. The LFO signal is the output of an underdamped system with high quality factor and high-resonance property at a specific frequency. The high-resonance component is the denoised LFO signal, and the residual contains most of the noise. Secondly, the high-resonance component is decomposed by FSWT and the full band of its time-frequency distribution are obtained. The 3D map expression and dominant mode of the LFO can be obtained. After that, due to its energy distribution, frequency slices are chosen to get accurate analysis of time-frequency features. Through reconstructing signals in characteristic frequency slices, separation and extraction of the LFO mode components is realized. Thirdly, high-accuracy detection for modal parameter identification is achieved by the Hilbert transform. Simulation and application examples prove the effectiveness of the proposed method.
Highlights
Low frequency oscillations (LFOs) arising from the interconnection of local power systems has caused more and more concerns over the years
It can analyze the oscillation character from multiple angles, which includes the determination of dominant mode, mode separation and extraction, and the 3D map expression of the LFO signal
resonance-based sparse signal decomposition (RSSD) consists of two parts: a tunable Q-factor wavelet transform (TQWT) and morphological component analysis (MCA)
Summary
Low frequency oscillations (LFOs) arising from the interconnection of local power systems has caused more and more concerns over the years. FSWT can very clearly represent the damping characteristics of multi-modal signals simultaneously in the time and frequency domains [7,8,9], which is very suitable for dealing with LFO signals It can analyze the oscillation character from multiple angles, which includes the determination of dominant mode, mode separation and extraction, and the 3D map expression of the LFO signal. RSSD is a sparsity-enabled signal analysis method proposed by Selesnick [10] It is a new nonlinear signal analysis method based on signal resonance rather than on frequency or scale, as provided by the Fourier and wavelet transforms. FSWT is used to decompose the high-resonance component and the full band of its time-frequency distribution can be obtained, along with the 3D map expression and dominant mode of the LFO. High-accuracy detection for modal parameter identification is achieved by the Hilbert transform
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