Electric power system low frequency oscillation mode online identification method

Abstract

The invention discloses an electric power system low frequency oscillation mode online identification method comprising the steps that firstly generator angular velocity signals recorded by an electric power system wide area measurement system under the normal operation situation are read firstly; then de-mean processing is performed on the read generator angular velocity signals; then zero mean signals are inputted to a signal processing module based on the random decrement technology so that free oscillation signals are acquired; and finally model identification is performed on the free oscillation signals based on a least square complex exponential method so that the frequency and the damping ratio of the electric power system low frequency oscillation mode can be identified. According to the method, the electric power system low frequency oscillation mode can be identified in a wider range of time and the method has great identification accuracy and anti-noise performance so that a brand-new approach and method can be provided for electric power system low frequency oscillation analysis.

Description

technical field [0001] The invention relates to the field of power system stability analysis, in particular to an online identification method for a low-frequency oscillation mode of a power system. Background technique [0002] Interregional low-frequency oscillation poses a serious threat to the safe and stable operation of power systems. Accurate and rapid identification of low-frequency oscillation modes is of great significance to the analysis and control of low-frequency oscillations in power systems. [0003] The traditional low-frequency oscillation analysis method is to establish a detailed system state space model, linearize the model near a certain operating point, and obtain the frequency, damping ratio and mode information of the oscillation by solving the eigenvalue problem of the system state matrix. Because the system model cannot be updated in real time, the oscillation mode of the system obtained by this analysis method may be quite different from the oscil...

AI Technical Summary

Problems solved by technology

[0005] Among the methods based on random response signals, at present, the stochastic subspace method (SSI) requires a large number of singular value decompositions, resulting in slow calculation speed and easy to generate false patterns; the method based on the ARMA model is difficult to accurately identify the damping ratio; The reduction technology combined with Prony's method has poor noise immunity, resulting in inaccurate identification results

It can be seen that the deficiencies of the existing technology include that the required power system input signal cannot be measured, the amount of calculation is large, or the method has poor noise immunity, which leads to inaccurate identification results.

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