Time-Frequency Analysis of Non-stationary Phenomena in Electrical Engineering
نویسندگان
چکیده
This paper serves the idea of applying joint time-frequency representations in electrical engineering. Main directions of researches are concentrated around Cohen’s class of transformation which gives some possibilities of adaptation for analysed signal by choosing appropriate kernel function. Additionally, novel approach delivered by S-transform is also introduced. In order to investigate the methods several experiments were performed using simulated phenomena of switching on the capacitor banks in distribution system. Firstly some aspect of S-transform application were present. Then the influence of different kernel functions were investigated when Wigner-Ville, ChoiWilliams and Zhao-Atlas-Marks distributions were applied. Obtained results were supplemented by comparison to classical spectrogram. Proposed methods allowed to track instantaneous frequency as well as energy with better time-frequency precision than classical spectrogram. It leads to applications in diagnosis and power quality area.
منابع مشابه
Synchrosqueezing-based Transform and its Application in Seismic Data Analysis
Seismic waves are non-stationary due to its propagation through the earth. Time-frequency transforms are suitable tools for analyzing non-stationary seismic signals. Spectral decomposition can reveal the non-stationary characteristics which cannot be easily observed in the time or frequency representation alone. Various types of spectral decomposition methods have been introduced by some resear...
متن کاملA Time-Frequency approach for EEG signal segmentation
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...
متن کاملAn Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform
In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...
متن کاملAn Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform
In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...
متن کاملA new adaptive exponential smoothing method for non-stationary time series with level shifts
Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...
متن کامل