METHOD FOR IDENTIFICATION OF ELECTRICAL SIGNAL DISORDERS BASED ON ADISCRETE WAVELET TRANSFORM

2020 ◽  
Vol 2 (1) ◽  
pp. 016-021
Author(s):  
B. Yu. Kisselyov ◽  
◽  
L. A. Faifer ◽  
G. Yu. Kisselyov ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Electrical Signal

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

Plant Electrical Signal De-Noising Based on the Lifting Wavelet Transform

2015 ◽  
Vol 727-728 ◽  
pp. 900-903
Author(s):  
Yu Shuang Li ◽  
Li Guo Tian ◽  
Jiang Lin Wei ◽  
Meng Li ◽  
Jiang Tao Zou
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transformation ◽  
Aloe Vera ◽  
Experimental System ◽  
Electrical Signal ◽  
Future Research ◽  
Lifting Wavelet Transform ◽  
Weak Signals ◽  
Lifting Wavelet

In this paper, plant electrical signal of Aloe vera L. was collected by experimental system which was built in the laboratory of writer .And used MATLAB to de-noise processing and analyzing. We adopted both traditional and lifting wavelet transformation respectively, compared to two results, we can know that analyses which based on lifting wavelet transformation are more accurate and quick ,It is more suitable for weak signals in future research and analysis.

Study of On-Line Detection of Voltage Transient Disturbance Based on DSP and Wavelet

2014 ◽  
Vol 664 ◽  
pp. 288-292
Author(s):  
Xi Xia Huang ◽  
Fei Wang ◽  
Cheng Hou ◽  
Bing Zhang
Keyword(s):  
Wavelet Transform ◽  
Data Processing ◽  
Real Time ◽  
Digital Signal Processor ◽  
Electrical Signal ◽  
Discrete Wavelet ◽  
Processing Unit ◽  
On Line ◽  
Transient Voltage ◽  
Disturbance Detection

In this paper, a set of voltage disturbance detection device based on DSP2812 was designed and a method of transient voltage disturbance detection based on DSP and Wavelet Transform was studied. The device collects electrical signal through a Hall sensor and the parallel A/D converter and regards TMS320F2812, a kind of high-performance digital signal processor (DSP), as a core data processing unit. Discrete Wavelet Transform (DWT) algorithm on DSP board was carried out to detect transient voltage disturbance. Software modules including the main program module, A/D module, interrupt module, communication module and so on was designed. The DWT algorithm on DSP board which could detect transient voltage disturbance on line was carried out based on the strong operation ability of DSP and the high effectiveness of DWT algorithm. This device can simultaneously realize power acquisition and voltage disturbance analysis in real-time. Contrast tests show that the device is of high-precision, of high-data-processing-speed and has a capability of voltage disturbance detecting in real-time.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

Analysis of magneto rheological elastomers for energy harvesting systems

2020 ◽  
Vol 64 (1-4) ◽  
pp. 439-446
Author(s):  
Gildas Diguet ◽  
Gael Sebald ◽  
Masami Nakano ◽  
Mickaël Lallart ◽  
Jean-Yves Cavaillé
Keyword(s):  
Magnetic Field ◽  
Energy Harvesting ◽  
Shear Strain ◽  
Magnetic Induction ◽  
Magnetic Particles ◽  
Homogeneous Magnetic Field ◽  
Electrical Signal ◽  
Harvesting Systems ◽  
Dc Bias ◽  
Magneto Rheological

Magneto Rheological Elastomers (MREs) are composite materials based on an elastomer filled by magnetic particles. Anisotropic MRE can be easily manufactured by curing the material under homogeneous magnetic field which creates column of particles. The magnetic and elastic properties are actually coupled making these MREs suitable for energy conversion. From these remarkable properties, an energy harvesting device is considered through the application of a DC bias magnetic induction on two MREs as a metal piece is applying an AC shear strain on them. Such strain therefore changes the permeabilities of the elastomers, hence generating an AC magnetic induction which can be converted into AC electrical signal with the help of a coil. The device is simulated with a Finite Element Method software to examine the effect of the MRE parameters, the DC bias magnetic induction and applied shear strain (amplitude and frequency) on the resulting electrical signal.

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