Abstract
The vibration signals of wind turbines are often disturbed by strong noise and will be annihilated when exhibiting fault or strong instability. Denoising is required prior to facilitating an analysis of vibration fault characteristics. A wavelet denoising method based on variational mode decomposition (VMD) and multiscale permutation entropy (MPE) is proposed. The characteristics of VMD are analyzed, and the randomness and complexity of noise are evaluated by MPE. If the MPE of the modal component after VMD is larger than the evaluation value, then it is denoised by wavelet, and the signal is reconstructed with other modal components without wavelet denoising to achieve the denoising effect. The db1, sym8, EMD and EWT denoising methods and the proposed method are compared using the same simulation signal. Simulation results show that the denoising effect of the proposed method is better than the other four methods, and the two quantitative evaluation indexes of relative error and root mean square error obtain desirable values. The shaft vibration signals of the Case Western Reserve University and wind turbine are used to verify the effectiveness of the proposed method, and the denoising effect is also better than the other four methods. The proposed method eliminates most of the noise components while retaining the effective information of the signal. Therefore, this method can provide a good foundation for the research and analysis of the characteristics of late vibration signal.
Highlights
In recent years, wind energy has received increasing attention as a clean and renewable energy source; wind turbines have developed rapidly around the world
Chen et al.: Wavelet Denoising for the Vibration Signals of Wind Turbines Based on variational mode decomposition (VMD) and multiscale permutation entropy (MPE)
To preserve the useful information in the signal while removing the fault signal noise, a wavelet denoising method based on VMD and MPE is proposed in this study
Summary
Wind energy has received increasing attention as a clean and renewable energy source; wind turbines have developed rapidly around the world. X. Chen et al.: Wavelet Denoising for the Vibration Signals of Wind Turbines Based on VMD and MPE mathematical morphology, wavelet, enhanced stochastic resonance, local mean decomposition (LMD), empirical wavelet transform (EWT) and independence-oriented variational mode decomposition filtering methods [6]–[15]. For nonlinear non-Gaussian models, it is difficult to obtain a complete analytical expression of the probability density function, and only a few approximation algorithms can be used to obtain the optimal Bayesian estimation It could only obtain optimal filtering when the statistical characteristics of the signal and noise were known. 3) To obtain the optimal solution of the constrained variational problem in Step 2, the Lagrangian multiplication operator λ(t) and the second penalty factor αare introduced; α can ensure the reconstruction accuracy of the signal in the presence of Gaussian noise, and λ(t) maintains the strictness of the constraints. A larger Hp denotes a more random time series, whereas a smaller Hp implies a more regular time series
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
