Reassigned Time Frequency Analysis for PD Signals in GIS

2013 ◽  
Vol 448-453 ◽  
pp. 1959-1962
Author(s):  
Hui Wang ◽  
Xiu Wei Li ◽  
Yu Xin Yun ◽  
Hai Yan Yuan
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Varying ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
The Cross ◽  
Non Stationary Signal

Partial discharge signal in GIS is a kind of typical non-stationary signal, using the time or frequency domain simply is not enough to describe the time-varying information of PD. Based on the reason above, this paper introduces a joint time-frequency analysis method according to the reassignment theory for analyzing the PD of GIS. After the processing of the PD signals simulated and on field, we conclude that this method provides a higher concentration in the time-frequency plane and reduces the most influence of the cross-interference terms.

Chirp Signal Time-Frequency Analysis Characteristic Comparison

2012 ◽  
Vol 226-228 ◽  
pp. 568-571 ◽  
Author(s):  
Jing Lei Zhou ◽  
Fan Wang
Keyword(s):  
Frequency Analysis ◽  
Chirp Signal ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Signal Noise ◽  
Time Frequency ◽  
Sine Signal ◽  
Non Stationary Signal ◽  
Signal Time ◽  
Selection Of

Chirp signal is a typical non-stationary signal, and have been widely used in communication, sonar, radar and so on. So, this signal is worth to analysis. In order to show the characteristics, this paper first introduces the definition and formula of each algorithm, then with all kinds of time-frequency analysis method to the signals, and the signal to add two sine signal noise are analyzed, the comparison of the characteristics of the method in the paper, and the signal for the analysis, the selection of an appropriate analysis. Through analysis and comparison, when dealing with the signal, Hilbert-Huang transformation not only has a better gathered characteristic, but also has a better resolution to distinguish the sine signal noise. Finally, use the MATLAB software simulation to obtain the result.

The Applied Research of Time-Frequency Analysis Method Employed in Seismic Signal Processing

2014 ◽  
Vol 955-959 ◽  
pp. 1809-1812
Author(s):  
Zuo Ju Wu ◽  
Zhi Jia Wang ◽  
Jun Wei Bi
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Seismic Signal ◽  
Frequency Domain Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Seismic Signal Processing

In the traditional processing of seismic signal, the frequency domain analysis method is always available to research some features which always vary with frequency. However, the condition of parameters which vary with time going can’t be considered in this method. So all the information in time domain have been neglected. In this article, time-frequency analysis method called HHT(Hilbert-Huang transform) is applied to analyze the Qingping wave of Wenchuan earthquake meticulously, which is the most advantaged to dissect the change features of the seismic record at different scales. Then we can get the dual properties in time domain and the frequency domain, such as the IMF function of each modal and the instantaneous frequency. For reflecting the time-frequency characteristics exactly and clearly, the Hilbert spectrum has been used to show these messages in the time-frequency plane.

Blind Separation of Non-Stationary Convoluted Mixtures Based on Time-Frequency Analysis

2012 ◽  
Vol 538-541 ◽  
pp. 2571-2575
Author(s):  
Peng Wang ◽  
Ji Hua Cao ◽  
Xiao Chang Ni
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Varying ◽  
Blind Separation ◽  
Frequency Method ◽  
Distribution Method ◽  
Time Frequency Analysis ◽  
Joint Diagonalization ◽  
Time Frequency ◽  
Analysis Methods

The signals of convoluted mixtures have a stated of non-stationary identity, and the change of their spectrum with time-varying usually could not be observed from the frequency domain, but they can be observed by the time-frequency method. Therefore, the blind separation of non-stationary convoluted mixtures based on time-frequency analysis is proposed in this paper. For the non-stationary identity, the space-albinism of the mixed matrices and the joint diagonalization of the time-frequency matrices are simulated to separate the convoluted mixtures. Two kinds of time-frequency analysis methods, Wigner-Ville distribution and improved Wigner-Ville distribution, are introduced, which are calculated with MATLAB 7.0 software. The simulated results show the improved Wigner-Ville distribution method has a better performance for blind separating of non-stationary convoluted and mixed signals.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency
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