scholarly journals Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy

2021 ◽  
Vol 13 (17) ◽  
pp. 9868
Author(s):  
Dan Su ◽  
Kaicheng Li ◽  
Nian Shi
Keyword(s):  
Renewable Energy ◽  
Power Quality ◽  
Frequency Distribution ◽  
Window Function ◽  
Energy Concentration ◽  
Time Frequency ◽  
Gaussian Window ◽  
S Transform ◽  
Power Quality Disturbances ◽  
Time Frequency Distribution

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.

scholarly journals Analysis of Power Quality Signals Using an Adaptive Time-Frequency Distribution

Energies ◽  
2016 ◽  
Vol 9 (11) ◽  
pp. 933 ◽  
Author(s):  
Nabeel Khan ◽  
Faisal Baig ◽  
Syed Nawaz ◽  
Naveed Ur Rehman ◽  
Shree Sharma
Keyword(s):  
Power Quality ◽  
Frequency Distribution ◽  
Time Frequency ◽  
Quality Signals ◽  
Adaptive Time ◽  
Time Frequency Distribution

An Evaluation of Linear Time Frequency Distribution Analysis for VSI Switch Faults Identification

2017 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
M.F. Habban ◽  
M. Manap ◽  
A.R. Abdullah ◽  
M.H. Jopri ◽  
T. Sutikno
Keyword(s):  
Fault Detection ◽  
Frequency Distribution ◽  
Linear Time ◽  
Harmonic Distortion ◽  
Voltage Source ◽  
Distribution Analysis ◽  
Voltage Source Inverter ◽  
Time Frequency ◽  
S Transform ◽  
Time Frequency Distribution

This paper present an evaluation of linear time frequency distribution analysis for voltage source inverter system (VSI). Power electronic now are highly demand in industrial such as manufacturing, industrial process and semiconductor because of the reliability and sustainability. However, the phenomenon that happened in switch fault has become a critical issue in the development of advanced. This causes problems that occur study on fault switch at voltage source inverter (VSI) must be identified more closely so that problems like this can be prevented. The TFD which is STFT  and S-transform method are analyzed the switch fault of VSI.  To identify the VSI switches fault, the parameter of fault signal such as instantaneous of average current, RMS current, RMS fundamental current, total waveform distortion, total harmonic distortion and total non-harmonic distortion can be estimated from TFD. The analysis information are useful especially for industrial application in the process for identify the switch fault detection. Then the accuracy of both method, which mean STFT and S-transform are identified by the lowest value of mean absolute percentage error (MAPE). In addition, the S-transform gives a better accuracy compare with STFT and it can be implement for fault detection system.

Power Quality Disturbances Classification Based on Modified S Transform and Decision Tree

2013 ◽  
Vol 860-863 ◽  
pp. 1891-1894
Author(s):  
Ji Liang Yi ◽  
Ou Yang Qin
Keyword(s):  
Decision Tree ◽  
Power Quality ◽  
Statistical Features ◽  
Time Frequency ◽  
S Transform ◽  
Novel Method ◽  
Power Quality Disturbances ◽  
Frequency Properties

A novel method for power quality disturbances classification is presented using modified S transform (MST) and decision tree. The time-frequency properties of power quality disturbances are analyzed and the effects of window-wide parameter g on the properties are discussed. Four statistical features are extracted from the MST module time-frequency matrix and a decision tree is utilized to recognize 9 power quality disturbances. The simulations are made to illustrate the validity of the method proposed for power quality disturbances recognition.

Sparsity-promoting method to estimate the dispersion curve of surface-wave group velocity

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. V33-V43 ◽  
Author(s):  
Reza Dokht Dolatabadi Esfahani ◽  
Roohollah Askari ◽  
Ali Gholami
Keyword(s):  
Surface Wave ◽  
Group Velocity ◽  
Energy Concentration ◽  
Wave Group ◽  
Velocity Range ◽  
S Wave ◽  
New Approach ◽  
Time Frequency ◽  
Gaussian Window ◽  
S Transform

Group velocity is an important characteristic of surface wave that is defined as the velocity of an envelope of frequencies. Although many studies have shown the promises of analyzing the group velocity to obtain subsurface S-wave velocity, the estimation of the group velocity is not straightforward due to the uncertainties of selecting an optimum envelope of frequencies. Conventional transformations or filtering algorithms used to define an optimum envelope usually give reasonable results just for a narrow frequency or velocity range. We introduced a new approach for the estimation of the group velocity using the sparse S transform (SST) and sparse linear Radon transform (SLRT). In SST, the width of the Gaussian window is optimally calculated by energy concentration to eliminate energy smearing in the time-frequency (TF) domain, and then the sparsity is applied to enhance the TF resolution. Compared with conventional methods for the estimation of the group velocity based on the generalized S transforms, SST does not require any adjustment to the Gaussian window and yields accurate estimates of the group velocity. We apply SST to each seismic trace of a seismic shot record to obtain a 3D cube of frequency, time, and offset. For any frequency, we obtain a common frequency gather of time and offset to which we apply SLRT to obtain the group velocity of the surface wave. Our approach is robust at calculating high-resolution distinguishable dispersion curves of the group velocity in particular when data are extremely sparse.

Improved S-Transform for Time-Frequency Analysis for Power Quality Disturbances

2021 ◽  
pp. 1-1
Author(s):  
Chengbin Liang ◽  
Zhaosheng Teng ◽  
Jianmin Li ◽  
Wenxuan Yao ◽  
Lei Wang ◽  
...  
Keyword(s):  
Power Quality ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbances

Rapid Power Quality Detection Based on Generalized HS-Transform

2014 ◽  
Vol 568-570 ◽  
pp. 223-226
Author(s):  
Guan Qi Liu ◽  
Li Na Wu
Keyword(s):  
Power Quality ◽  
Frequency Regulation ◽  
Complex Matrix ◽  
Practical Applications ◽  
Time Frequency ◽  
Quality Detection ◽  
S Transform ◽  
Power Quality Disturbances ◽  
Low Sensitivity ◽  
Computation Speed

In this paper, the hyperbolic S-transform (HS-transform) has been generalized with the introduction of the whole time frequency regulation factor and making the HS window function proportional to the square root of the frequency. Meanwhile, combined with the idea of incomplete S-transform, a rapid power quality detection based on generalized HS-transform (GHST) is proposed. Firstly, the fast Fourier transform (FFT) was performed and dynamic measurement was utilized to describe the envelope of power spectrum so as to detect the valid frequency samples of FFT. Then the GHST was specifically performed for these samples and a complex matrix was generated. Finally, these feature vectors extracted from the complex matrix were used to detect the power quality disturbances. Simulation results demonstrate that the proposed method can accurately detect the power quality disturbances with high computation speed and low sensitivity to noise, and it is suitable for practical applications.

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