A prce-based online identification method for low-frequency oscillation modes

Abstract

The invention discloses a PRCE-based low-frequency oscillation mode online identification method, which belongs to the field of low-frequency oscillation of power systems. Including: first read the angular velocity measurement signals of multiple generators after a power system disturbance; then construct the Hankel matrix from the free oscillation signal, and then solve the column vector by the least square method; finally construct the matrix polynomial to solve the eigenvalue matrix and the right eigenvector Matrix, so as to obtain the oscillation frequency, damping ratio and oscillation mode of the system. The method of the present invention is based on multi-channel signals, can identify oscillation modes, and the identification results are more stable; compared with the TLS-ESPRIT method and the SSI method, the identification results of the method of the present invention are better in terms of identification accuracy and efficiency.

Description

technical field [0001] The invention relates to the field of low-frequency oscillations in electric power systems, in particular to a method for online identification of low-frequency oscillation modes based on PRCE. Background technique [0002] Low-frequency oscillation is an important issue in the stable operation of power systems. Rapid and accurate identification of low-frequency oscillation modes is of great significance to the analysis and control of low-frequency oscillations in power grids. Traditional analysis methods need to establish a detailed mathematical model for the system. For large-scale and complex interconnected grids, there are certain difficulties in accurate modeling. The massive installation of PMUs (Synchronized Phasor Measurement Units) in power systems enables low-frequency oscillation analysis based on wide-area measurement data. Since the measured data truly reflects the current operating state of the system, the measurement-based low-frequency...

AI Technical Summary

Problems solved by technology

In the prior art, the stochastic subspace method (Stochastic Subspace Identification) SSI is also applied to the electromechanical oscillation pattern recognition of power systems. This method is based on multi-channel signals, can identify oscillation modes, and has strong noise immunity. Singular value decomposition is performed on large matrices with relatively small dimensions, so its calculation speed is slow and it is prone to spurious patterns

[0005] Therefore, in the prior art, there are technical problems such as the inability to calculate the oscillation mode, low identification accuracy, poor noise immunity, and slow calculation speed.

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