Pressure fluctuation signal analysis of a hydraulic turbine based on variational mode decomposition

Abstract

Due to the nonstationarity of the pressure fluctuation signal from a hydraulic turbine draft tube, this research proposes a pressure fluctuation signal analysis method based on variational mode decomposition (VMD). Firstly, the research was used to compare VMD with empirical mode decomposition (EMD) by using a simulation signal, which shows that the VMD method could effectively inhibit mode mixing, but EMD method could not. This could improve the accuracy of such analysis. Then, the VMD method was applied to analyze the real pressure fluctuation signal from a hydraulic turbine. The results showed that VMD produced clearer time–frequency spectra. This allowed accurate analysis of pressure fluctuation signals. This research provided a new method of pressure fluctuation signal analysis for hydraulic turbine draft tubes.

Keywords

Hydraulic turbine pressure fluctuation signal variational mode decomposition empirical mode decomposition mode mixing time–frequency spectrum

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References

Gao

Deng

. Numerical simulation of three-dimensional unsteady vortex rope turbulent flow occurred to the draft tube of a Francis turbine. J Hydraulic Eng 2009; 40: 1162–1167.

Zhang

Gao

Pan

. Study on pressure fluctuation in Francis turbine draft tubes during partial load. J Hydraulic Eng 2011; 42: 1234–1238.

Jia

Zhang

. Extract dynamic specific property information of hydraulic turbine based on characteristic entropy of dual-tree complex wavelet. J Hydroelectr Eng 2012; 31: 292–296.

Huang

. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adapt Data Anal 2009; 1: 1–41.

Jiang

Zhao

. Short-term prediction of wind power using EMD and chaotic theory. Commun Nonlinear Sci Numer Simul 2012; 17: 1036–1042.

Dragomiretskiy

Zosso

. Variational mode decomposition. IEEE Trans Signal Process 2014; 62: 531–544.

Mohanty

Gupta

. Bearing fault analysis using variational mode decomposition. J Instrum Technol Innov 2014; 4: 1–8.

Guo

AL-Shudeifat

Yan

. Application of empirical mode decomposition to a Jeffcott rotor with a breathing crack. J Sound Vib 2013; 332: 3881–3892.

Zhao

Wang

. Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 2013; 49: 279–288.

10.

Amarnath

Praveen Krishna

. Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis. Measurement 2014; 58: 154–164.

11.

Zheng

Cheng

Nie

. Complete ensemble local characteristic-scale decomposition and its applications to rotor fault diagnosis. J Vib Eng 2014; 27: 637–646.

12.

Arpe

Nicolet

Avellan

. Experimental evidence of hydroacoustic pressure waves in a Francis turbine elbow draft tube for low discharge conditions. J Fluids Eng 2009; 131: 081102–19.