Research on the Noise Reduction Method of the Vibration Signal of the Hydrogenerator Unit Based on ITD-PE-SVD. (8th September 2021)

• Record Type: Journal Article
• Authors: Ren, Yan; Liu, Pan; Hu, Leiming; Qiao, Ruoyu; Zhang, Linlin; Huang, Shaojie
• Other Names: Wang Xian-Bo Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-09-08
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/9589412
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Aiming at the problem that the vibration signals of the hydrogenerator unit are nonlinear and nonstationary and it is difficult to extract the signal features due to strong background noise and complex electromagnetic interference, this paper proposes a dual noise reduction method based on intrinsic time-scale decomposition (ITD) and permutation entropy (PE) combined with singular value decomposition (SVD). Firstly, the vibration signals are decomposed by ITD to obtain a series of PRC components, and the permutation entropy of each component is calculated. Secondly, according to the set permutation entropy threshold, the PRC components are selected for reconstruction to achieve a noise reduction effect. On this basis, SVD is carried out, and the appropriate reconstruction order is selected according to the position of the singular value difference spectrum mutation point for reconstruction, so as to achieve the secondary noise reduction effect. The proposed method is compared with the LMD-PE-SVD and EMD-PE-SVD dual noise reduction method by simulation, taking the correlation coefficient and signal-to-noise ratio to evaluate the noise reduction performance and finding that the ITD-PE-SVD noise reduction has good noise reduction and pulse effect. Furthermore, this method is applied to the analysis of the upper guide swing data in the X -direction and Y -direction of a unit in a hydropower station in China, and it is found that this method can effectively reduce noise and accurately extract signal features, thus determining the vibration cause, which is helpful to improve the turbine fault recognition rate.