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Power quality disturbances classification using wavelet transform and support vector machine

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dc.contributor.affiliationen_US
dc.contributor.authorMuhammad Hazwan Bin Harunen_US
dc.date.accessioned2023-05-03T15:24:08Z
dc.date.available2023-05-03T15:24:08Z
dc.date.issued2018
dc.description.abstractAutomatic 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.en_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/20817
dc.language.isoenen_US
dc.subjectWavelets (Mathematics)en_US
dc.titlePower quality disturbances classification using wavelet transform and support vector machineen_US
dc.typeResource Types::text::Thesisen_US
dspace.entity.typePublication
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