Power quality disturbances classification using wavelet transform and support vector machine
Automatic classification of Power Quality Disturbances is a challenging concern for utility provider and industry. A technique for classification of hybrid Power Quality Disturbances is proposed based on Wavelet Transform and Support Vector Machine.2 types of Wavelet Transform are used on the genera...
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my.uniten.dspace-208172023-05-05T15:38:50Z Power quality disturbances classification using wavelet transform and support vector machine Muhammad Hazwan Bin Harun Wavelets (Mathematics) Automatic classification of Power Quality Disturbances is a challenging concern for utility provider and industry. A technique for classification of hybrid Power Quality Disturbances is proposed based on Wavelet Transform and Support Vector Machine.2 types of Wavelet Transform are used on the generated Power Quality Disturbances signal, in order to decompose the signal. Features Extraction were applied to the wavelet sub band. These parameters are used as features vector for the classifier. Our database consists of 200 samples for each PQD totaling 2400 generated signals of PQD. 2023-05-03T15:24:08Z 2023-05-03T15:24:08Z 2018 Resource Types::text::Thesis https://irepository.uniten.edu.my/handle/123456789/20817 en application/pdf |
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| language |
English |
| topic |
Wavelets (Mathematics) |
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Wavelets (Mathematics) Muhammad Hazwan Bin Harun Power quality disturbances classification using wavelet transform and support vector machine |
| description |
Automatic classification of Power Quality Disturbances is a challenging concern for utility provider and industry. A technique for classification of hybrid Power Quality Disturbances is proposed based on Wavelet Transform and Support Vector Machine.2 types of Wavelet Transform are used on the generated Power Quality Disturbances signal, in order to decompose the signal. Features Extraction were applied to the wavelet sub band. These parameters are used as features vector for the classifier. Our database consists of 200 samples for each PQD totaling 2400 generated signals of PQD. |
| format |
Resource Types::text::Thesis |
| author |
Muhammad Hazwan Bin Harun |
| author_facet |
Muhammad Hazwan Bin Harun |
| author_sort |
Muhammad Hazwan Bin Harun |
| title |
Power quality disturbances classification using wavelet transform and support vector machine |
| title_short |
Power quality disturbances classification using wavelet transform and support vector machine |
| title_full |
Power quality disturbances classification using wavelet transform and support vector machine |
| title_fullStr |
Power quality disturbances classification using wavelet transform and support vector machine |
| title_full_unstemmed |
Power quality disturbances classification using wavelet transform and support vector machine |
| title_sort |
power quality disturbances classification using wavelet transform and support vector machine |
| publishDate |
2023 |
| _version_ |
1806428351459491840 |
| score |
13.211869 |