Low Frequency Mode Estimation of a Dynamic Power System by Noise Assisted Empirical Mode Decomposition

The original Empirical Mode Decomposition (EMD)is incapable of separating closely spaced frequency component of power system signal because it suffers from mode mixing problem. This paper proposes a noise-assisted Empirical Mode Decomposition technique which can effectively improve the mode mixing problem. The proposed methodologies in this paperare first applied to an artificial test signal having similar nature like dynamic power system oscillatory wave to verify the ability in theseparation of mixing mode. Thereafter the real time data of Eastern Interconnect Phasor Project (EIPP), U.S.Aare analyzed. Further different modal frequency components are extracted by EMD, Ensemble Empirical Mode Decomposition(EEMD), Complete Ensemble Empirical Decomposition (CEEMDAN) which are then compared. Also, Hilbert spectrum analysis is carried out to compare frequency variation of various extracted signals. From the simulation results, it is concluded that EEMD technique works well in fixing mode mixing problem than previously used EMD based techniques but the problem of noise in the extracted modes of EEMD still remains which is overcome by CEEMDAN technique.