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Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network

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dc.citedby0
dc.contributor.authorTan R.H.G.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorid35325391900en_US
dc.contributor.authorid6602912020en_US
dc.date.accessioned2023-12-29T07:53:16Z
dc.date.available2023-12-29T07:53:16Z
dc.date.issued2009
dc.description.abstractThree phase multi pulse rectifier classification using scale selection wavelet and probabilistic neural network is presented in this paper. The scale selection wavelet selectively perform continuous wavelet transform on the desired scales, which are determined by the scale frequency relationship to precisely locate each harmonic center frequency for harmonic analysis. Thus, the continuous wavelet transform selectively transform only the 16 characteristic harmonic frequencies of interest from 2nd to 25th order, which are required for three phase multi pulse rectifier classification. The 16 characteristic harmonic frequencies energy are used as the input vector to the probabilistic neural network to classify 5 types of three phase multi pulse rectifier including 3, 6, 12, 18 and 24 pulse converter. Various sets of harmonic distortion signals are used to evaluate the performance of these wavelet and neural network based classification system. The results show excellent performance in terms of high accuracy in classifying harmonic distortion caused by three phase multi pulse rectifier. These harmonic classification information serves as guideline to develop and optimize mitigation solution to reduce harmonic disturbance and resonance problem in the industry facility.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5385786
dc.identifier.doi10.1109/PEDS.2009.5385786
dc.identifier.epage811
dc.identifier.scopus2-s2.0-77950885378
dc.identifier.spage806
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77950885378&doi=10.1109%2fPEDS.2009.5385786&partnerID=40&md5=dc57f7f24b2b8f3758778edd2ec30bf7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30787
dc.pagecount5
dc.sourceScopus
dc.sourcetitleProceedings of the International Conference on Power Electronics and Drive Systems
dc.subjectHarmonic
dc.subjectMulti pulse rectifier
dc.subjectPower quality
dc.subjectProbabilistic neural network
dc.subjectWavelet transform
dc.subjectElectric rectifiers
dc.subjectFourier series
dc.subjectHarmonic analysis
dc.subjectHarmonic distortion
dc.subjectPower electronics
dc.subjectWavelet transforms
dc.subject24-pulse
dc.subjectCenter frequency
dc.subjectClassification system
dc.subjectContinuous Wavelet Transform
dc.subjectExcellent performance
dc.subjectHarmonic
dc.subjectHarmonic disturbances
dc.subjectHarmonic frequency
dc.subjectInput vector
dc.subjectMulti-pulse rectifiers
dc.subjectProbabilistic neural networks
dc.subjectResonance problem
dc.subjectScale selection
dc.subjectThree phase
dc.subjectNeural networks
dc.titleMulti pulse rectifier classification using scale selection wavelet & probabilistic neural networken_US
dc.typeConference paperen_US
dspace.entity.typePublication
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