Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection
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 t...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/2/174 |
| _version_ | 1797494551510777856 |
|---|---|
| author | Sihan Chen Ziche Li Guobing Pan Fang Xu |
| author_facet | Sihan Chen Ziche Li Guobing Pan Fang Xu |
| author_sort | Sihan Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:35:56Z |
| format | Article |
| id | doaj.art-e290b77a0f724b668b588c5f30765beb |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T01:35:56Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e290b77a0f724b668b588c5f30765beb2023-11-23T13:33:27ZengMDPI AGElectronics2079-92922022-01-0111217410.3390/electronics11020174Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature SelectionSihan Chen0Ziche Li1Guobing Pan2Fang Xu3College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaWith 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.https://www.mdpi.com/2079-9292/11/2/174power qualityempirical wavelet transformfeature selectionpattern recognitiondisturbance detection |
| spellingShingle | Sihan Chen Ziche Li Guobing Pan Fang Xu Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection Electronics power quality empirical wavelet transform feature selection pattern recognition disturbance detection |
| title | Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection |
| title_full | Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection |
| title_fullStr | Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection |
| title_full_unstemmed | Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection |
| title_short | Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection |
| title_sort | power quality disturbance recognition using empirical wavelet transform and feature selection |
| topic | power quality empirical wavelet transform feature selection pattern recognition disturbance detection |
| url | https://www.mdpi.com/2079-9292/11/2/174 |
| work_keys_str_mv | AT sihanchen powerqualitydisturbancerecognitionusingempiricalwavelettransformandfeatureselection AT zicheli powerqualitydisturbancerecognitionusingempiricalwavelettransformandfeatureselection AT guobingpan powerqualitydisturbancerecognitionusingempiricalwavelettransformandfeatureselection AT fangxu powerqualitydisturbancerecognitionusingempiricalwavelettransformandfeatureselection |