Analysis of Sensor Trapped Power Quality Indices Using Empirical Wavelet Transform and Rational Dilation Wavelet Transform to Achieve High Accuracy and Frequency Resolution

2019 ◽  
Vol 17 (4) ◽  
pp. 323-336 ◽  
Author(s):  
Srividhya Jayaraman Panneerselvam ◽  
Sivakumar Dhandapani
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
High Accuracy ◽  
Frequency Resolution ◽  
Quality Indices ◽  
Empirical Wavelet Transform ◽  
Rational Dilation

Visualizing time-varying power quality indices using generalized empirical wavelet transform

2017 ◽  
Vol 143 ◽  
pp. 99-109 ◽  
Author(s):  
Karthik Thirumala ◽  
Shantanu ◽  
Trapti Jain ◽  
Amod C. Umarikar
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Time Varying ◽  
Quality Indices ◽  
Empirical Wavelet Transform

scholarly journals Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 174
Author(s):  
Sihan Chen ◽  
Ziche Li ◽  
Guobing Pan ◽  
Fang Xu
Keyword(s):  
Feature Selection ◽  
Wavelet Transform ◽  
Power Quality ◽  
Electrical Equipment ◽  
Support Vector ◽  
Analysis Tool ◽  
Electrical Systems ◽  
Empirical Wavelet Transform ◽  
Power Quality Disturbance ◽  
System Calculation

With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

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