Abstract
The most classical subsynchronous oscillation (SSO) mode extraction methods have some shortcomings, such as lower mode identification and poor anti-noise properties. Thus, this paper proposes a new time-frequency analysis method, namely, synchrosqueezed wavelet transforms (SWT). SWT combines the advantages of empirical mode decomposition (EMD) and wavelet, which has the adaptability of EMD, and improve the ability of anti-mode mixing on EMD and wavelet. Thus, better anti-noise property and higher mode identification can be achieved. Firstly, the SSO signal is transformed by SWT and its time-frequency spectrum is obtained. Secondly, the attenuation characteristic of each intrinsic mode type (IMT) component in its time-frequency spectrum is analyzed by an automatic identification algorithm, and determine which IMT component needs reconstruction. After that, the selected IMT components with divergent characteristic are reconstructed. Thirdly, high-accuracy detection for mode parameter identification is achieved by the Hilbert transform (HT). Simulation and application examples prove the effectiveness of the proposed method.
Highlights
Subsynchronous oscillation (SSO) is a classic problem in power systems
A new time-frequency analysis method using synchrosqueezed wavelet transforms (SWT) and Hilbert transform (HT) is applied to the parameter identification of SSO in power systems
(1) As an empirical mode decomposition (EMD)-like tool, SWT overcomes the problem of mode mixing in EMD and energy parameter identification of in power systems
Summary
Subsynchronous oscillation (SSO) is a classic problem in power systems. A steam turbine and electric generator can have mechanical SSO modes between the various turbine stages and the generator. Energies 2018, 11, 1525 through a phasor measurement unit (PMU) in actual electrical networks often contain some noise This requires that the mode extraction method have a high mode resolution and some anti-noise ability. Synchrosqueezed wavelet transform (SWT) is a new time-frequency analysis method proposed by Daubechies et al in 2011 [7]. She called it: an empirical mode decomposition (EMD)-like tool. The anti-noise ability and time-frequency resolution of SWT are improved on the basis of wavelet transform (WT). SWT is adaptive like EMD and does not depend on the mother wavelet; the mode mixing problem is improved greatly; SWT has good anti-noise ability. High-accuracy detection for mode parameter identification is achieved by the Hilbert transform (HT)
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