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Power Quality Disturbances Classification Using Dimensionality

Evolution of the current modern era demands a huge and good power quality supply day by day. Power utility and power trade service suppliers encounters difficult issues in identifying and sorting the Power Quality Disturbances (PQD). My thesis illustrates the technique of PQD classification by utili...

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Main Author: Prakash a/l Bala
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Language:English
Published: 2023
Subjects:
Power Quality
Machine Learning
PCA
KPCA
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spelling my.uniten.dspace-205692023-05-05T05:41:11Z Power Quality Disturbances Classification Using Dimensionality Prakash a/l Bala Power Quality Machine Learning PCA KPCA Evolution of the current modern era demands a huge and good power quality supply day by day. Power utility and power trade service suppliers encounters difficult issues in identifying and sorting the Power Quality Disturbances (PQD). My thesis illustrates the technique of PQD classification by utilizing wavelet decomposition, two methods of dimensional reduction, Principal Component Analysis (PCA) and Kernel Principal Component Analysis (Kernel PCA) with a choice of classifier, the multiclass k-Nearest Neighbors (k-NN). A normal wave without disturbance and waves with PQD events of single-type and hybrid-type were generated using Python with Scikit Learn package using the mathematical model as per the definition and parameters outlined by IEEE 1159 and IEC61000 customary. The generated PQD signals was processed with Discrete Wavelet Transform (DWT), to decompose and acquire it’s illustration in time and frequency domain. In this project work, my database consists of 14000 generated signals of a normal wave and the PQDs, which were divided into 80% of train set and 20% of test set for each PQDs. An k-nearest method for the multiclass classifier with a choice of mother wavelet filter function was worked out with the PQD’s feature vector. Main idea of this research was to enhance the output performance of the classifier after applying dimensional reduction method. By training the classifier to observe and analyze the performance, different tests was carried out. The final result outputs the performance variation between training the k-NN classifier versus training the k-NN classifier in dimension reduction. The k-NN classifier with dimensional reduction succeed to classify the PQDs with higher accuracy in both train and test set. 2023-05-03T15:06:05Z 2023-05-03T15:06:05Z 2019-10 https://irepository.uniten.edu.my/handle/123456789/20569 en application/pdf
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/
language English
topic Power Quality
Machine Learning
PCA
KPCA
spellingShingle Power Quality
Machine Learning
PCA
KPCA
Prakash a/l Bala
Power Quality Disturbances Classification Using Dimensionality
description Evolution of the current modern era demands a huge and good power quality supply day by day. Power utility and power trade service suppliers encounters difficult issues in identifying and sorting the Power Quality Disturbances (PQD). My thesis illustrates the technique of PQD classification by utilizing wavelet decomposition, two methods of dimensional reduction, Principal Component Analysis (PCA) and Kernel Principal Component Analysis (Kernel PCA) with a choice of classifier, the multiclass k-Nearest Neighbors (k-NN). A normal wave without disturbance and waves with PQD events of single-type and hybrid-type were generated using Python with Scikit Learn package using the mathematical model as per the definition and parameters outlined by IEEE 1159 and IEC61000 customary. The generated PQD signals was processed with Discrete Wavelet Transform (DWT), to decompose and acquire it’s illustration in time and frequency domain. In this project work, my database consists of 14000 generated signals of a normal wave and the PQDs, which were divided into 80% of train set and 20% of test set for each PQDs. An k-nearest method for the multiclass classifier with a choice of mother wavelet filter function was worked out with the PQD’s feature vector. Main idea of this research was to enhance the output performance of the classifier after applying dimensional reduction method. By training the classifier to observe and analyze the performance, different tests was carried out. The final result outputs the performance variation between training the k-NN classifier versus training the k-NN classifier in dimension reduction. The k-NN classifier with dimensional reduction succeed to classify the PQDs with higher accuracy in both train and test set.
format
author Prakash a/l Bala
author_facet Prakash a/l Bala
author_sort Prakash a/l Bala
title Power Quality Disturbances Classification Using Dimensionality
title_short Power Quality Disturbances Classification Using Dimensionality
title_full Power Quality Disturbances Classification Using Dimensionality
title_fullStr Power Quality Disturbances Classification Using Dimensionality
title_full_unstemmed Power Quality Disturbances Classification Using Dimensionality
title_sort power quality disturbances classification using dimensionality
publishDate 2023
_version_ 1806427649091829760
score 13.222552

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