Synchrosqueezing Wavelet Transform based Identification of Transient Events in AC Microgrid

Author(s):  
Ayushi Gupta ◽  
K. Seethalekshmi
2020 ◽  
Vol 41 (2) ◽  
pp. 61-67
Author(s):  
Marko Tončić ◽  
Petra Anić

Abstract. This study aims to examine the effect of affect on satisfaction, both at the between- and the within-person level for momentary assessments. Affect is regarded as an important source of information for life satisfaction judgments. This affective effect on satisfaction is well established at the dispositional level, while at the within-person level it is heavily under-researched. This is true especially for momentary assessments. In this experience sampling study both mood and satisfaction scales were administered five times a day for 7 days via hand-held devices ( N = 74 with 2,122 assessments). Several hierarchical linear models were fitted to the data. Even though the amount of between-person variance was relatively low, both positive and negative affect had substantial effects on momentary satisfaction on the between- and the within-person level as well. The within-person effects of affect on satisfaction appear to be more pronounced than the between-person ones. At the momentary level, the amount of between-person variance is lower than in studies with longer time-frames. The affect-related effects on satisfaction possibly have a curvilinear relationship with the time-frame used, increasing in intensity up to a point and then decreasing again. Such a relationship suggests that, at the momentary level, satisfaction might behave in a more stochastic manner, allowing for transient events/data which are not necessarily affect-related to affect it.


1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura

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.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian

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.


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