Hydroelectric generating set vibration signal noise reduction method based on mode autocorrelation analysis of SVD and VMD

Abstract

A hydroelectric generating set vibration signal noise reduction method based on mode autocorrelation analysis of SVD and VMD comprises the steps of constructing a Hankel matrix of a set vibration signal and performing singular value decomposition (SVD), selecting an effective singular value based on a mean value filtering strategy for reconstructing the signal, and realizing pre-filtering; performing decomposition through variational mode decomposition (VMD) for obtaining a series of mode functions, calculating an autocorrelation function of each mode component, selecting effective mode components according to an energy set of the autocorrelation function, and obtaining a signal after noise reduction through adding all effective mode components. According to the hydroelectric generating set vibration signal noise reduction method provided by the invention, a noise reduction experiment is carried out through simulation analysis and actual measurement of a vibration signal; and a result represents a fact that the method has relatively high noise reduction performance and can effectively improve hydroelectric generating set vibration signal analysis precision.

Description

technical field [0001] The invention belongs to the field of state monitoring and signal analysis of a hydroelectric unit in an electric power system, in particular to a vibration signal noise reduction method for a hydroelectric unit based on SVD and VMD modal autocorrelation analysis. Background technique [0002] As the core equipment of power plant operation, the health status of hydroelectric unit is not only related to the safety of the power plant, but also affects the safety and stability of the regional large power grid. Therefore, it is of great engineering practical significance to study the stability of the unit. In the stability analysis of the unit, vibration is an important index to characterize the health state of the unit. However, due to the influence of strong background noise and complex electromagnetic interference, the vibration characteristic frequency band representing the fault information will be lost in the background noise of the whole frequency b...

AI Technical Summary

Problems solved by technology

Among them, the noise reduction effect of wavelet transform depends on the selection of wavelet base and the determination of threshold value; the noise reduction effect of SVD is related to the construction of Hankel matrix and the selection of effective singular values; although EMD can adaptively decompose the signal into multiple modes state function, but due to the problem of modal aliasing, the separation effect of noise and useful signal is not ideal; VMD is a new method of adaptive quasi-orthogonal signal decomposition proposed by Dragomiretskiy et al. in 2014, which recursively solves the The sub-problem decomposes the signal into a set of modal function sets with limited bandwidth, realizes the separation of the frequency of each signal component, and overcomes the modal aliasing problem existing in EMD. , its analysis accuracy is difficult to guarantee

Images