Abstract
One of the main reasons that affecting the stability of power systems is low frequency oscillation (LFO). The existence of noise influences the accuracy of LFO mode identification extracted from wide-area measurement system (WAMS). The wavelet threshold de-noising is widely used in signal processing. In this paper, wavelet soft threshold is illustrated to attenuate the noise of LFO signal, the optimal wavelet basis and decomposition level for de-noising LFO signal with noise are obtained and verified by experiments. Following the signal de-noising, an improved Matrix Pencil (MP) algorithm is used for mode identification of LFO. This improvement particularly lies in the ratio of adjacent singular entropy increment difference (RASEID) designed as an adaptive order determination method in the MP algorithm proposed in the paper. RASEID not only makes the MP algorithm adaptive, but also enhances the stability of the order determination in the mode identification process. The proposed method ensures the accuracy of mode identification with lower sensitivity to noise interference. Finally, the validity of the proposed method is verified by three cases studies. The first study is on the analysis of synthetic signal typically performed in many literatures. The second study is to identify the mode of active power LFO signal which is generated by IEEE four-generator and two-area system given with disturbance on RT-LAB experimental platform. The third study is the oscillation analysis of the actual LFO data in the North American power grid. The results validate the feasibility of the proposed method for mode identification of noisy LFO.
Highlights
It is well known that low-frequency oscillation (LFO) is an inherent phenomenon of power systems [1]
This paper has proposed a novel adaptive Matrix Pencil (MP) algorithm based on wavelet soft-threshold de-noising to deal with the LFO signal extracted from the wide-area measurement systems (WAMS) in power systems
The main advantage of the proposed de-noising method lies in the appropriate wavelet basis and decomposition level
Summary
It is well known that low-frequency oscillation (LFO) is an inherent phenomenon of power systems [1]. The performance of different wavelet bases and decomposition levels are tested and compared to choose the best parameters matching the noise reduction of LFO signal. In order to determine the best parameters more reasonably in the process of wavelet soft-threshold de-noising for LFO signal, the studies on the selections of the best wavelet basis and wavelet decomposition level are carried out in the paper. In order to analyze and select the best wavelet bases for LFO de-noising, the number of wavelet decomposition levels is assumed to be 3 All these wavelet bases are tested to the same noisy LFO signal x(n) so that the best wavelet basis will be found and selected according to the maximal SNR and the minimal MSE. Based on the above discussion, wavelet basis Coif and decomposition level 4 are the best parameter matching way and have obvious advantages of wavelet soft-threshold de-noising to the noisy LFO signal.
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