In this paper, a novel method that integrates the improved empirical mode decomposition （EMD） and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Fir...In this paper, a novel method that integrates the improved empirical mode decomposition （EMD） and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Firstly, the EMD with symmetrical extrema extension （SEE） is utilized to decompose the measured data from wide area measurement system （WAMS） into a finite set of intrinsic mode functions （1MFs）. Then, the signal energy algorithm is used to calculate the approximate oscillation parameters of the IMFs. The nodes involved the dominant oscillation mode are classified based on the calculated frequency and reasonable threshold. Furthermore, for the dominant oscillation mode, the IMF with maximum mean amplitude is defined as the reference. Next, the relative phases （RPs） between the reference IMF and other 1MFs are calculated in order to identify the negative and positive oscillation groups. According to the values of RPs, the coherent group and corresponding node contribution factor （NCF） can be identified, and the dominant approximate mode shape （AMS） can also be determined. The efficiency of the proposed approach is tested by applying it to synthetic signal and measured data from the simulation model.展开更多
文摘In this paper, a novel method that integrates the improved empirical mode decomposition （EMD） and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Firstly, the EMD with symmetrical extrema extension （SEE） is utilized to decompose the measured data from wide area measurement system （WAMS） into a finite set of intrinsic mode functions （1MFs）. Then, the signal energy algorithm is used to calculate the approximate oscillation parameters of the IMFs. The nodes involved the dominant oscillation mode are classified based on the calculated frequency and reasonable threshold. Furthermore, for the dominant oscillation mode, the IMF with maximum mean amplitude is defined as the reference. Next, the relative phases （RPs） between the reference IMF and other 1MFs are calculated in order to identify the negative and positive oscillation groups. According to the values of RPs, the coherent group and corresponding node contribution factor （NCF） can be identified, and the dominant approximate mode shape （AMS） can also be determined. The efficiency of the proposed approach is tested by applying it to synthetic signal and measured data from the simulation model.
