Fourth-order mixed mean cumulant and improved TLS-ESPRIT algorithm-based low-frequency oscillation mode identification method

Abstract

The invention relates to a fourth-order mixed mean cumulant and improved TLS-ESPRIT algorithm-based low-frequency oscillation mode identification method. Gaussian color noise is inhibited by using a fourth-order mixed cumulant, a TLS-ESPRIT algorithm is improved, a relative change rate of a singular value is introduced for order determination, and a matrix is constructed by using measured data to carry out low-frequency oscillation mode identification of a power system. According to the fourth-order mixed mean cumulant and improved TLS-ESPRIT algorithm-based low-frequency oscillation mode identification method, detected oscillation frequencies, attenuation factors, oscillation amplitudes and phase information of low-frequency oscillation signals of the power system in different oscillation modes are more accurate.

Description

technical field [0001] The invention relates to the field of safe operation of power systems, in particular to a low-frequency oscillation mode identification method based on fourth-order mixed average cumulant and improved TLS-ESPRIT algorithm. Background technique [0002] With the continuous advancement of power technology, the interconnection of large power grids has been gradually realized, coupled with the widespread use of fast and high-magnification excitation devices, resulting in low-frequency oscillations from time to time. Power system low-frequency power oscillation is a kind of active power oscillation that occurs in the power system, and the oscillation frequency is usually 0.2Hz-2.5Hz. The problem of low-frequency oscillation affects the safety and stability of the power system. Therefore, effective and rapid identification of low-frequency oscillation modes and research on corresponding low-frequency oscillation suppression strategies and methods are the key...

AI Technical Summary

Problems solved by technology

The FFT method has good accuracy and robustness, but this method cannot effectively extract the attenuation characteristics of the oscillation; wavelet analysis has the problem that the wavelet basis is difficult to select and it is difficult to achieve fast online identification; Under the noise condition of the linear-to-noise ratio, there will be a large error in the identification accuracy; HHT analysis is suitable for the extraction of nonlinear and non-stationary signal characteristic parameters. The disadvantage is that there will be endpoint effects and modal aliasing in the process of signal extraction. And the complexity of the algorithm is high, which is not suitable for online identification; ESPRIT method based on space rotation invariant technology, this method has better anti-interference ability and calculation efficiency than Prony

[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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