scholarly journals Research on Noise Reduction Method of Pressure Pulsation Signal of Draft Tube of Hydropower Unit Based on ALIF-SVD

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan Ren ◽  
Pan Liu ◽  
Leiming Hu ◽  
Jin Huang ◽  
Ruoyu Qiao ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Pressure Fluctuation ◽  
Reduction Method ◽  
Draft Tube ◽  
Sample Entropy ◽  
Singular Value ◽  
Hydropower Station ◽  
Stable Operation ◽  
Reconstructed Signal ◽  
Reduction Effect

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.

scholarly journals Research on the Noise Reduction Method of the Vibration Signal of the Hydrogenerator Unit Based on ITD-PE-SVD

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yan Ren ◽  
Pan Liu ◽  
Leiming Hu ◽  
Ruoyu Qiao ◽  
Linlin Zhang ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Electromagnetic Interference ◽  
Reduction Method ◽  
Recognition Rate ◽  
Difference Spectrum ◽  
Singular Value ◽  
Permutation Entropy ◽  
Vibration Signals ◽  
Signal Features ◽  
Reduction Effect

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.

Discussion on the Wavelet Adjustment Factor Threshold Noise Reduction Technology Based on DSP

2011 ◽  
Vol 317-319 ◽  
pp. 1201-1204
Author(s):  
Zi Ran Liu ◽  
Tao He ◽  
Yuan Yuan He ◽  
Yu Xi Yu
Keyword(s):  
Signal Processing ◽  
Noise Reduction ◽  
High Performance ◽  
Vibration Signal ◽  
Adjustment Factor ◽  
Reconstructed Signal ◽  
Actual Signal ◽  
Soft Threshold ◽  
Reduction Effect ◽  
Fixed Bias

Because of discontinuity at threshold, hard threshold de-noising leads to the additional oscillation at threshold when reconstructing the signal, so that the smoothness of the de-noised signal becomes weak; Soft threshold de-noising method can guarantee the good continuity of the signal. However, for the existence of the fixed bias between the estimated threshold and actual threshold, there are also deviations between the reconstructed signal and the actual signal existing deviations too. In this work, by using the high performance DSP as a signal processing tool, we reduced the noise of actual measured vibration signal collected from headstock of CA6140 lathe. By comparing, the result is the same as the noise reduction simulation calculated from MATLAB, which demonstrates the noise reduction effect of wavelet adjustment factor threshold de-noising apparently.

scholarly journals Intelligent alarm data noise reduction for power systems based on singular value decomposition

2021 ◽  
Vol 2108 (1) ◽  
pp. 012074
Author(s):  
Chen Chen ◽  
Hongren Man ◽  
Xiu Liu
Keyword(s):  
Data Structure ◽  
Singular Value Decomposition ◽  
Power System ◽  
Noise Reduction ◽  
Reduction Method ◽  
Singular Value ◽  
Data Noise ◽  
Noise Data ◽  
Value Decomposition ◽  
Noise Statistics

Abstract The noise types of power system intelligent alarm data are complex. When reducing the intelligent alarm data, the profile noise statistics of the noise data are large, resulting in the actual noise reduction value is too small. To solve this problem, a power system intelligent alarm data noise reduction method based on singular value decomposition is designed. The selected normalized decomposition matrix iteratively processes the original matrix, the singular value decomposes the power system alarm data, sets an estimation quantity within the paradigm of the alarm data, controls the noise profile noise statistics, characterizes the noise alarm data structure, uses the SC algorithm to process the cluster basis vectors in the noise data structure, and constructs a repeated iterative convergence process to realize intelligent data noise reduction processing. The original alarm data within a known power system is used as test data, the power system alarm window is set, and the power system alarm data singular values are circled. The data mining-based alarm data noise reduction method, the regularized filter-based alarm data noise reduction method and the designed data noise reduction method are applied to the noise reduction process, and the results show that the designed data noise reduction method has the largest noise value and the best noise reduction effect.

Research on the Pressure Fluctuation in Draft Tube Based on the Sparse Bayesian

2010 ◽  
Vol 108-111 ◽  
pp. 696-699
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Ji Gao Zhang
Keyword(s):  
Pressure Fluctuation ◽  
Draft Tube ◽  
Kernel Method ◽  
Relevance Vector Machine ◽  
Kernel Functions ◽  
Hydropower Station ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Actual Situation ◽  
Unit Discharge

Relevance Vector Machine (RVM) is a novel kernel method based on Sparse Bayesian, which has many advantages such as its kernel functions without the restriction of Mercer’s conditions, the relevance vectors automatically determined and fewer parameters. In view of the actual situation that the corresponding space curved surface which expressed the characteristics of pressure fluctuation is too complex to be analyzed, the RVM regression model for describing the characteristics which is nonlinear relationship among pressure fluctuation value，unit rotation and unit discharge is established，and it is applied to hydropower station. Comparing with Support Vector Machine (SVM), the experimental results show the final RVM model achieved is sparser, the prediction precision is higher and the prediction values are in better agreement with the real values.

scholarly journals Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition

2020 ◽  
Vol 10 (4) ◽  
pp. 1409
Author(s):  
Gang Zhang ◽  
Benben Xu ◽  
Kaoshe Zhang ◽  
Jinwang Hou ◽  
Tuo Xie ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Noise Pollution ◽  
Singular Value ◽  
Reduction Model ◽  
Mode Decomposition ◽  
Starting Point ◽  
Reduction Effect ◽  
Value Decomposition

Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

Pressure Fluctuation Based on Cascade Correlation Algorithm in Draft Tube

2010 ◽  
Vol 121-122 ◽  
pp. 38-42
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pressure Fluctuation ◽  
Network Architecture ◽  
Draft Tube ◽  
Hydropower Station ◽  
Actual Situation ◽  
Final Model ◽  
Correlation Algorithm ◽  
Cascade Correlation

The cascade-correlation(CC) is presented as a neural network growing technique which allows one to gradually build network architecture without the need to redefine the number of neurons to be used in a feed forward. In view of the actual situation that the corresponding space curved surface which expresses pressure fluctuation in draft tube is too complex to be analyzed, considering the pressure fluctuation in draft tube, the network model is established based on CC algorithm and it is applied to hydropower station. Comparing with BP neural network, the experimental results show the prediction precision of the final model is higher and the prediction values are in better agreement with the real values.

The Noise Analysis for Draft Tube of Turbine

2011 ◽  
Vol 42 (10) ◽  
pp. 41-46
Author(s):  
Dehua Wei ◽  
Weiguo Zhao
Keyword(s):  
Frequency Spectrum ◽  
Field Test ◽  
Test Data ◽  
Time Domain ◽  
Pressure Fluctuation ◽  
Draft Tube ◽  
Noise Analysis ◽  
Hydropower Station ◽  
Hydraulic Pressure ◽  
Noise Elimination

We take the field test data from the entrance to the draft tube of the turbine of a hydropower station as an example, the signals acquired are depicted in time domain and frequency spectrum, and the relation between the noise and the load is analyzed, then we find out the reasons which cause noise and put forward the measures for noise elimination. After analyzing the noise signals of the draft tube under different loads, the experimental results show that when the unit operates in load 19MW - 80MW and 138MW - 179MW, the noise is much larger than usual, the abnormal noise is mainly caused by the hydraulic pressure fluctuation of draft tube, the best way to eliminate the noise is the reasonable air compensation for the draft tube.

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