A Global Thresholding De-Noising Method for Power Quality Signals

2015 ◽  
Vol 733 ◽  
pp. 662-665
Author(s):  
Lin Lin ◽  
Xiao Huan Wu ◽  
Jia Jin Qi ◽  
Hong Xin Ci ◽  
Zhi Yong Yu
Keyword(s):  
Power Quality ◽  
Transform Method ◽  
Noise Component ◽  
New Approach ◽  
Two Dimension ◽  
Quality Signals ◽  
S Transform ◽  
The Matrix ◽  
Simulation Results ◽  
Global Thresholding

The noise component in power quality signals affects the accuracy of analysis result. This paper presents a new approach for power quality signals de-noising. Firstly, the original signals are transformed by S-transform method. Then, the matrix which is get from S-transform result is processed as a two-dimension image. The global thresholding de-noising method is used to filtering the noise component in the power quality signals. The simulation results showed the effectiveness of the new approach.

Harmonic Detection in Power Signals Based on Fast Modified S-Transform

2014 ◽  
Vol 986-987 ◽  
pp. 1510-1513
Author(s):  
Guan Qi Liu ◽  
Li Na Wu ◽  
Zhao Lei Qin
Keyword(s):  
Dynamic Measurement ◽  
High Accuracy ◽  
Harmonic Amplitude ◽  
Harmonic Detection ◽  
Complex Matrix ◽  
Harmonic Frequency ◽  
New Approach ◽  
S Transform ◽  
The Matrix ◽  
Simulation Results

In this paper, a new approach for harmonic detection in power signals based on fast modified S-transform (FMST) is proposed, which combines the advantages of the incomplete S-transform and the modified S-transform. Initially, the Fast Fourier Transform was performed and dynamic measurement values were obtained from the envelope of power spectrum, then the valid harmonic frequency points were detected by selecting the dynamic measurement values which were larger than the set thresholds. Further, the FMST was specifically performed on these major frequency points and a complex matrix was generated. The row vectors of the matrix reflected the phase and the time location information of harmonic disturbances, while the column vectors reflected the amplitude-frequency characteristics. And the feature vectors extracted from the complex matrix were used to detect the harmonic amplitude, phase and transient information. Simulation results validate the high accuracy, strong noise immunity and rapidity of the proposed approach.

Harmonic Analysis Based on Modified Hyperbolic S-Transform

2015 ◽  
Vol 733 ◽  
pp. 906-909
Author(s):  
Lin Lin ◽  
Jia Jin Qi ◽  
Xiao Huan Wu ◽  
Hong Xin Ci ◽  
Shang Qun Yang
Keyword(s):  
Harmonic Analysis ◽  
Harmonic Component ◽  
New Method ◽  
New Approach ◽  
High Noise ◽  
Noise Environment ◽  
Harmonic Control ◽  
S Transform ◽  
Simulation Results

Harmonic analysis is the foundation of harmonic control and compensation. The voltage signals with harmonic component is difficult to analysis under noise environment. This paper proposed a new approach for harmonic analysis based on Hyperbolic S-transform. Firstly, the affection for harmonic analysis by different characters of the hyperbolic window including forward-taper parameter and backward-taper parameter is compared. Secondly, the modified Hyperbolic S-transform with optimal characters is used for harmonic analysis. Finally, the analysis result of the new approach is compared with other methods. Simulation results show the effectiveness and advantages of the new method. It is very satisfied for harmonic analysis under high noise environments.

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.

Classification of Power Quality Disturbance Based on Continuous S-Transform-Windowing Technique (CST-WT) and ANOVA as a Feature Selection

2015 ◽  
Vol 785 ◽  
pp. 368-372 ◽  
Author(s):  
Kamarulazhar Daud ◽  
Ahmad Farid Abidin ◽  
Harapajan Singh Nagindar Singh ◽  
Mohd Najib Mohd Hussain
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Transient Signal ◽  
New Approach ◽  
Power Quality Monitoring ◽  
S Transform ◽  
Power Quality Disturbance ◽  
Before And After ◽  
Windowing Technique

This paper was conducted in order to identify and classify the different types of Power Quality Disturbances (PQD) based on a new approach the Analysis Of Variance (ANOVA). ANOVA is used as feature selection for the PQD parameters. The datum of PQD from the PSCAD/EMTDC® simulation and Power Quality Monitoring has been validated before feature extraction analysis can be commenced. The obtained datum is then analyzed by using Windowing Technique (WT) based on Continuous S-Transform (CST) to extract the features and its characteristics. Moreover, the study focuses an important issue concerning the identification of PQD selection, detection and classification. The feature and characteristics of three types of signal such as sag, swell, and transient signal are obtained. The outcome of the analysis shows that a new approach framework ANOVA-Based Before and After Neural Network (NN) classification has a slightly increases to 15-25% in term of classification of PQD.

The Application of Similarity Measure for Power Quality Waveform Distortion Detection

2010 ◽  
Vol 439-440 ◽  
pp. 304-308
Author(s):  
Lin Lin ◽  
Jia Jin Qi ◽  
Nan Tian Huang
Keyword(s):  
Power Quality ◽  
Similarity Measure ◽  
Distance Measure ◽  
New Method ◽  
Quality Signals ◽  
Waveform Distortion ◽  
Simulation Results ◽  
Quality Diagnosis

Power quality waveform distortion detection is one of the most important works for power quality diagnosis. It is the basis to decide when to record the fault power quality signals. This paper presents a new method to detect waveform distortion by similarity measure. 6 types of typical power quality events are simulated to compare the effect of distortion detection. Simulation results show that the distance measure is suit to waveform distortion detection and normalized distance has the best effect of detection.

ANOVA Based Feature Analysis and Selection in Power Quality Disturbances Identification

2015 ◽  
Vol 793 ◽  
pp. 510-515
Author(s):  
Kamarulazhar Daud ◽  
Ahmad Farid Abidin ◽  
Harapajan Singh Nagindar Singh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Power Quality ◽  
Feature Analysis ◽  
New Approach ◽  
S Transform ◽  
Different Types ◽  
Power Quality Disturbances ◽  
Selection For ◽  
Windowing Technique

This study was conducted in order to identify the different types of PQD based on a new approach the Analysis Of Variance (ANOVA). ANOVA is used as feature selection for the Power Quality Disturbances (PQD) parameters. The datum of PQD from the PSCAD/EMTDC® simulation has been validated before feature extraction analysis can be commenced. The obtained datum is then analyzed by using cycle windowing technique based on Continuous S-Transform (CST) to extract the features and its characteristics. Moreover, the study focuses an important issue concerning the identification of PQD selection and detection. The feature and characteristics of four types of signal such as Sag, Swell, Transient and sinusoidal normal signal are obtained. The outcome of the analysis shows that a new approach ANOVA have a different result in term of identification of PQD.

A Kaiser Window-Based S-transform for Time-Frequency Analysis of Power Quality Signals

2021 ◽  
pp. 1-1
Author(s):  
Chengbin Liang ◽  
Zhaosheng Teng ◽  
Jianmin Li ◽  
Wenxuan Yao ◽  
Shiyan Hu ◽  
...  
Keyword(s):  
Power Quality ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Quality Signals ◽  
S Transform ◽  
Kaiser Window
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