Abstract
The problem of low frequency oscillations (LFOs) parameters identification in power system with the method of improved Hilbert-Huang transform (HHT) is addressed. LFOs probably cause the great potential threat to the stable operation of power system, so identifying the parameters of LFOs is very important to develop the control strategies of restraining oscillations. In this paper, the LFOs signal is first processed by empirical mode decomposition (EMD) which is improved by the approach of endpoint extension used AR data prediction, stop shifting criterion and end condition via energy difference factor. The improved EMD is able to avoid the energy leakage and damping loss and also make the intrinsic mode functions (IMFs) closer to the true oscillations. Then the approach of parameters identification using least squares (LS) fitting algorithm based on the results of IMFs’ Hilbert transform is presented. Finally, case studies demonstrate the effectiveness of improved HHT process and LS parameters identification methods used in LFOs analysis of power system.
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