An Anti Mode Mixing EMD Algorithm for Detecting the Characteristics of Low Frequency Oscillations in Power System

The dynamics of modern interconnected power system is characterized by low frequency oscillations (LFOs) which are produced as results of various disturbances such as changes in loads, tripping of lines, faults, and other discrete events. A data driven empirical mode decomposition (EMD) method is applied to the detection of low frequency oscillation modes from disturbed trajectory with its strong non-stationary signal processing capability, but the mode mixing phenomenon serious impact on the analysis credibility and accuracy of EMD method. In this paper, an anti mode mixing EMD composite algorithm is proposed for detecting the characteristics of LFOs in power system. First, the improved frequency heterodyne method is proposed to increase the spectral distance between adjacent mode components in order to meet the octave resolution requirements. Second, the wavelet singularity detection technology is proposed to determine the adaptive sliding analysis window for each mode, in which there implements the intermittency mixed modes separation and their nonstationary parameters identification. Finally, the analysis result of interconnected grid test case verify that the proposed algorithm can effectively overcome the impact of the mode mixing existed in EMD and improve the characteristics detection accuracy of LFOs characteristics.