Abstract
Aiming at the problem that it is difficult to extract the characteristics of the draft tube pressure fluctuation signal under the background of strong noise, this study proposes a dual noise reduction method based on adaptive local iterative filtering (ALIF) and singular value decomposition (SVD). First, perform ALIF decomposition of the signal to be decomposed to obtain a series of IMF components, calculate the sample entropy of each component, select some IMF components to reconstruct according to the set sample entropy threshold, and then perform SVD decomposition on the reconstructed signal, and according to the location of the singular value difference spectrum mutation point, the appropriate number of reconstructions is selected for reconstruction, so as to achieve the double noise reduction effect. The ALIF-SVD dual noise reduction method proposed in this study is compared with the single ALIF, EMD, and EMD-SVD dual noise reduction method through simulation, and the correlation coefficient, signal-to-noise ratio, and mean square error are used to evaluate the noise reduction. It is found that the ALIF-SVD dual noise reduction method avoids the phenomenon of modal aliasing in the decomposition process, effectively removes the noise, and can retain the useful information of the original signal, and the noise reduction effect is better. A unit of a hydropower station in China is further selected as the research object, and its draft tube pressure fluctuation data were analyzed for noise reduction. It was found that this method can accurately extract the signal characteristics under strong noise background, so as to determine the type of pressure fluctuation of the unit, which is helpful to improve the fault recognition rate of hydraulic turbines. And it provides some technical support for the safe and stable operation of hydropower units and the promotion of condition-based maintenance strategy and improves the intelligent level of hydropower station operation management.
Highlights
In recent years, China’s hydropower has been fully developed and has produced huge economic benefits, and the stability of hydropower unit operation has attracted more and more attention and research
Based on the above analysis, this study proposes a dual noise reduction method based on adaptive local iterative filtering (ALIF)-Singular value decomposition (SVD) for the fault feature extraction of the pressure pulsation signal of the tailrace pipe of hydropower units under strong noise background, combined with the advantages of adaptive iterative filtering, sample entropy, and singular value
By combining the advantages of adaptive local iterative filtering, sample entropy, and singular value decomposition, a dual denoising method based on ALIF-SVD is proposed to solve the problem that weak feature signals are difficult to extract under strong noise background
Summary
China’s hydropower has been fully developed and has produced huge economic benefits, and the stability of hydropower unit operation has attracted more and more attention and research. In order to overcome the shortcomings of the EMD method, the literature [8] proposed an adaptive signal decomposition method of local mean decomposition, which has a certain improvement in the number of iterations and operation speed compared with EMD, but the problem of modal aliasing was still not fundamentally solved. Based on the above analysis, this study proposes a dual noise reduction method based on ALIF-SVD for the fault feature extraction of the pressure pulsation signal of the tailrace pipe of hydropower units under strong noise background, combined with the advantages of adaptive iterative filtering, sample entropy, and singular value. Rough the simulation analysis and example verification, it is found that the method can effectively reduce the noise of the pressure pulsation signal of the tailrace pipe under the background of strong noise and avoid the modal aliasing in the decomposition process. The application of this method in the feature extraction of tailpipe pressure pulsation signal under strong noise background is summarized and prospected
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