Generalized morphological filtering and improved MP algorithm-based low-frequency oscillation mode identification method

Abstract

The invention relates to a generalized morphological filtering and improved MP algorithm-based low-frequency oscillation mode identification method, and provides a low-frequency oscillation mode identification method of a power system by combining generalized morphological filtering with an improved MP algorithm. De-noising processing is carried out on a low-frequency oscillation signal by using the generalized morphological filtering and then mode identification is carried out by adopting the improved MP algorithm. For the problem of order determination of the MP algorithm in the low-frequency oscillation mode identification process, order determination is carried out by adopting a normalized singular entropy; and the method is relatively small in calculated quantity, high in speed and relatively small in subjective factor influence during order determination. According to the generalized morphological filtering and improved MP algorithm-based low-frequency oscillation mode identification method, accurate identification of a low-frequency oscillation mode under noise interference can be achieved; the problem that the accuracy of low-frequency oscillation mode identification of the power system is seriously affected by noise due to the fact that noise interference is usually introduced in measurement and transmission links of the power system is solved; and the low-frequency oscillation mode identification method has a relatively good application prospect.

Description

technical field [0001] The invention relates to the safe and stable operation of a power system, in particular to a low-frequency oscillation mode identification method based on generalized morphological filtering and an improved MP algorithm. Background technique [0002] With the increasingly complex power grid operation mode and power grid structure, the interconnection of power systems across large areas, the long-distance transmission of large-capacity generator sets, and the interconnection of large power systems, low-frequency oscillations caused by weak or under-damping often occur. The stability problem of the system becomes more and more obvious, and the modal analysis of power system oscillation belongs to the category of small disturbance stability analysis. Therefore, how to identify the fault area and the dominant mode of low-frequency oscillation of the power system quickly and effectively in real time, reduce the impact of noise to the greatest extent, and st...

AI Technical Summary

Problems solved by technology

However, the essence of the MP algorithm is a linear approximation method, so some oscillation data recorded when the power system begins to experience large disturbances (that is, strong nonlinearity) cannot be well suited for MP analysis; the FFT algorithm The accuracy is high and the robustness is good, but it can only be applied to oscillation signals with one dominant mode; the HHT method will have endpoint effects and mode aliasing during the analysis process, which has a great impact on the identification effect Influence; the biggest defect of the Prony algorithm is that it is very sensitive to noise and its performance will decrease in the case of low signal-to-noise ratio, while MP has more advantages in anti-noise and other performance

[0004] The harmonic pollution in the power system is becoming more and more serious and complex. The data extracted from the field contains a lot of noise, and the existence of interference will affect the accuracy of the signal identification results.

At present, the modal identification of the power system generally eliminates the noise through the built-in singular value decomposition. In this case, the estimation of the noise is not enough, especially the noise processing ability is limited in the case of low signal-to-noise ratio.

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