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

Three 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...

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Main Authors:	Tan R.H.G., Ramachandaramurthy V.K.
Other Authors:	35325391900
Format:	Conference paper
Published:	2023
Subjects:	Harmonic Multi pulse rectifier Power quality Probabilistic neural network Wavelet transform Electric rectifiers Fourier series Harmonic analysis Harmonic distortion Power electronics Wavelet transforms 24-pulse Center frequency Classification system Continuous Wavelet Transform Excellent performance Harmonic disturbances Harmonic frequency Input vector Multi-pulse rectifiers Probabilistic neural networks Resonance problem Scale selection Three phase Neural networks
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id	my.uniten.dspace-30787
record_format	dspace
spelling	my.uniten.dspace-307872023-12-29T15:53:16Z Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network Tan R.H.G. Ramachandaramurthy V.K. 35325391900 6602912020 Harmonic Multi pulse rectifier Power quality Probabilistic neural network Wavelet transform Electric rectifiers Fourier series Harmonic analysis Harmonic distortion Power electronics Wavelet transforms 24-pulse Center frequency Classification system Continuous Wavelet Transform Excellent performance Harmonic Harmonic disturbances Harmonic frequency Input vector Multi-pulse rectifiers Probabilistic neural networks Resonance problem Scale selection Three phase Neural networks Three 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. Final 2023-12-29T07:53:16Z 2023-12-29T07:53:16Z 2009 Conference paper 10.1109/PEDS.2009.5385786 2-s2.0-77950885378 https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950885378&doi=10.1109%2fPEDS.2009.5385786&partnerID=40&md5=dc57f7f24b2b8f3758778edd2ec30bf7 https://irepository.uniten.edu.my/handle/123456789/30787 5385786 806 811 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Harmonic Multi pulse rectifier Power quality Probabilistic neural network Wavelet transform Electric rectifiers Fourier series Harmonic analysis Harmonic distortion Power electronics Wavelet transforms 24-pulse Center frequency Classification system Continuous Wavelet Transform Excellent performance Harmonic Harmonic disturbances Harmonic frequency Input vector Multi-pulse rectifiers Probabilistic neural networks Resonance problem Scale selection Three phase Neural networks
spellingShingle	Harmonic Multi pulse rectifier Power quality Probabilistic neural network Wavelet transform Electric rectifiers Fourier series Harmonic analysis Harmonic distortion Power electronics Wavelet transforms 24-pulse Center frequency Classification system Continuous Wavelet Transform Excellent performance Harmonic Harmonic disturbances Harmonic frequency Input vector Multi-pulse rectifiers Probabilistic neural networks Resonance problem Scale selection Three phase Neural networks Tan R.H.G. Ramachandaramurthy V.K. Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network
description	Three 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.
author2	35325391900
author_facet	35325391900 Tan R.H.G. Ramachandaramurthy V.K.
format	Conference paper
author	Tan R.H.G. Ramachandaramurthy V.K.
author_sort	Tan R.H.G.
title	Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network
title_short	Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network
title_full	Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network
title_fullStr	Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network
title_full_unstemmed	Multi pulse rectifier classification using scale selection wavelet & probabilistic neural network
title_sort	multi pulse rectifier classification using scale selection wavelet & probabilistic neural network
publishDate	2023
_version_	1806427815976894464
score	13.226497

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

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