The Application Study of S-Transform Modulus Time-frequency Matrix in Detecting Power Quality Transient Disturbance

2012 ◽  
Vol 11 (3) ◽  
pp. 354-358 ◽  
Author(s):  
Li Jiasheng ◽  
Hu Saichun ◽  
Xiao Weichu ◽  
Qiu Biao
Keyword(s):  
Power Quality ◽  
Application Study ◽  
Time Frequency ◽  
S Transform

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.

Time-Frequency Analysis Methods for Power Quality Disturbances Feature Representation

2010 ◽  
Vol 439-440 ◽  
pp. 298-303
Author(s):  
Lin Lin ◽  
Jia Jin Qi ◽  
Nan Tian Huang ◽  
Shi Guang Luo
Keyword(s):  
Power Quality ◽  
Frequency Analysis ◽  
Time Instant ◽  
Feature Representation ◽  
Quality Analysis ◽  
Status Report ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Short Time

Power quality (PQ) analysis is the foundation of power system automation. The premise of power quality analysis is feature representation of power quality events. Time-frequency analysis (TFA) is very suitable for nonstationary signals analysis. The TFA of a PQ signal is to determine the energy distribution along the frequency axis at each time instant. This paper provides a status report of feature representation for PQ events by TFA methods, including short time Fourier transform (STFT), wavelet transform (WT) and S-transform (ST), overview the basic TFA theories for PQ analysis and compare the effectiveness of different TFA methodology. The expectation is that further research and applications of these TFA algorithms will flourish for PQ feature representation in the near future. The analysis direction and emphasis of studying are also put forward.

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

scholarly journals STUDY OF TIME FREQUENCY TRANSFORMS APPLIED TO POWER QUALITY SIGNALS

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Richard Bini Almeida ◽  
Kenji Watanabe ◽  
Silvia Mara da Costa Campos Victer
Keyword(s):  
Power Quality ◽  
Fourier Transforms ◽  
Spectral Content ◽  
Time Frequency ◽  
Quality Signals ◽  
S Transform ◽  
C Programming ◽  
The Fourier Transform ◽  
Short Time ◽  
Over Time

This work presents a scientific study on Short-Time Frequency Transforms (STFT) with different windows, also called Windowed Fourier Transforms, applied to power quality signals.   Additionally, it deals with S transforms, with its frequency-dependent window.  The disturbances related to energy quality have non-stationary nature, in which the spectral content varies over time.   So, the Fourier Transform is not appropriate for such analysis, because it doesn’t show time locations, only information about existing frequencies in the signal.  Therefore, the spectral analysis by windowed transforms helps to identify and detect a series of defects associated to these power signals.  The motivation behind this document is to verify which window will provide a more precise identification of the characteristics of the disturbances in time-frequency domain.    For this work, synthetic signals were generated for some of these disturbances, and their spectra were compared considering Gaussian, Hann and Blackman windows, as well as the S transform. Based on the obtained results, it was verified that each transform presents different behaviours acording to the input signal,  except for the ones with Hann and Blackman windows, that showed similar spectra. For all of them, there is always a tradeoff between time and frequency resolutions. Therefore, the choice of the window must be done according to the desired outputs.  The Dev-C ++ ® IDE was used for C ++ programming, and the Gnuplot ® program for graphics generation.

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.

scholarly journals Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinsong Li ◽  
Hao Liu ◽  
Dengke Wang ◽  
Tianshu Bi
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Noise Immunity ◽  
Signal Classification ◽  
Classification Method ◽  
Classification Methods ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbance

The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.

Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform

2015 ◽  
Vol 752-753 ◽  
pp. 1343-1348
Author(s):  
Abdul Rahim Abdullah ◽  
Nur Hafizah Tul Huda Ahmad ◽  
N.A. Abidullah ◽  
N.H. Shamsudin ◽  
M.H. Jopri
Keyword(s):  
Fourier Transform ◽  
Power Quality ◽  
Quality Analysis ◽  
Data Logger ◽  
Real Power ◽  
The Real ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbances ◽  
The Impact

Power quality is main issue because of the impact to electricity suppliers, equipments, manufacturers and user.To solve the power quality problem, an analysis of power quality disturbances is required to identify and rectify any failures on power system. Most of researchers apply fourier transform in power quality analysis, however the ability of fourier transform is limited to spectral information extraction that can be applied on stationary disturbances. Thus, time-frequency analysis is introduced for analyzing the power quality distubances because of the limitation of fourier transform. This paper presents the analysis of real power quality disturbances using S-transform. This time-frequency distribution (TFD) is presented to analyze power quality disturbances in time-frequency representation (TFR). From the TFR, parameters of the disturbances such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total nonharmonic distortion (TnHD) and total waveform distortion (TWD) of the disturbances are estimated. The experimental of three phase voltage inverter and starting motor are conducted in laboratory to record the real power quality disturbances. The disturbances are recorded via data logger system which is mplemented using LabVIEW while the analysis is done using Matlab in offline condition. The results show that S-transform gives good performance in identifying, detecting and analyzing the real power quality disturbances, effectively.

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 Power quality disturbance classification based on time-frequency domain multi-feature and decision tree

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenjing Zhao ◽  
Liqun Shang ◽  
Jinfan Sun
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Frequency Domain ◽  
Power Quality ◽  
Classification Accuracy ◽  
Support Vector ◽  
Classification Rules ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbance

AbstractAccurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

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