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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.

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems