Keywords
subsynchronous oscillation, synchrosqueezed wavelet transforms, time frequency analysis, Kmeans clustering
Abstract
Power system has nonstationary and nonlinear characteristics of the subsynchronous oscillation (SSO), it is difficult for existing detection methods to capture the oscillation characteristics and the changing trend. In this paper, an oscillation detection approach, which combines Kmeans clustering and synchrosqueezed wavelet transform (SWT), is proposed to achieve the harmonic detection and analysis of subsynchronous oscillation. The antimodal aliasing ability and antinoise ability of the SWT are utilized to clearly and intuitively show the oscillation modes of the signals with noise. The frequency domain slicing is employed in the SWT to extract multiple oscillation modes for the reconstruction and the parameter identification. Considering that the SWT will squeeze the wavelet coefficients to the central frequency, the Kmeans clustering method is applied to calculate the central frequency of the oscillating signal. At the same time, the frequency interval of the signal can be automatically selected for reconstruction. Finally, the simulations are conducted to examine the effectiveness of the proposed method.
DOI
10.19781/j.issn.1673-9140.2021.04.017
First Page
132
Last Page
140
Recommended Citation
Liu, Shaofeng; Xu, Taishan; Bao, Yanhong; and Chen, Yingjie
(2021)
"Subsynchronous oscillation detection based on Kmeans clustering and frequency synchrosqueezing wavelet transforms,"
Journal of Electric Power Science and Technology: Vol. 36:
Iss.
4, Article 17.
DOI: 10.19781/j.issn.1673-9140.2021.04.017
Available at:
https://jepst.researchcommons.org/journal/vol36/iss4/17