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• W2371495510 abstract "A stochastic subspace identification (SSI) method based on data-driven is applied to identification of power system low frequency oscillation modes. Firstly, by means of wavelet transform the noise contained in each signal is eliminated, and then the DC component is also eliminated. Hankel matrix is built based on treated data. The state space model is computed by QR method, singular value decomposition and Kalman filter estimation. Finally the system low frequency oscillation modes parameters are obtained by the state matrix eigenvalues. The ideal signal, simulated signal and the WAMS data of power system are analyzed respectively by the method. Analysis results show that SSI method based on data-driven can identify the main oscillation of the system accurately and it can be applied to on-line identification of power system oscillation modes." @default.
• W2371495510 created "2016-06-24" @default.
• W2371495510 creator A5060116249 @default.
• W2371495510 date "2013-01-01" @default.
• W2371495510 modified "2023-09-23" @default.
• W2371495510 title "Data-driven based stochastic subspace identification for low frequency oscillation analysis" @default.
• W2371495510 hasPublicationYear "2013" @default.
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