Subsynchronous oscillation detection in wind power system using multivariate empirical mode decomposition
With the large-scale integration of wind power,power system subsynchronous oscillation(SSO)events occur frequently,which seriously threatens the safe and stable operation of the power grid.The accurate detection of SSO in wind power grid-connected system is of great significance to ensure the stable operation of the power systems.Most of the existing SSO detection methods are single-channel methods,which are difficult to take into account the global SSO characteristics of the systems.Therefore,this paper proposes a SSO detection method for wind power grid-connected system based on multivariate empirical mode decomposition(MEMD).Firstly,the multivariate empirical mode decomposition is performed on the measurements of wind power grid-connected points,and then the IMF components with SSO mode are screened out via Teager-Kaiser energy operator(TKEO).Then,the Hilbert-Huang transform(HHT)is used to identify the SSO frequency and damping ratio.Finally,the proposed detection method is analyzed by the improved 4-machine 2-area system simulation data,and the results verify the effectiveness of the proposed method.
subsynchronous oscillation detectionmeasurementsmultivariate empirical mode decompositionTeager-Kaiser energy operatorHilbert-Huang transform
万方数据
维普
