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Effect of Signal Decomposition in Power Quality Disturbances Classification

FYP 2 SEM 2 2019/2020

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Main Author: Kavines a/l Murugesu
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Language:English
Published: 2023
Subjects:
singular spectrum analysis
power quality disturbances
machines learning
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spelling my.uniten.dspace-212942023-05-04T19:48:39Z Effect of Signal Decomposition in Power Quality Disturbances Classification Kavines a/l Murugesu singular spectrum analysis power quality disturbances machines learning FYP 2 SEM 2 2019/2020 The Revolution of the present modern era demands an unprecedented and good day-today supply of power quality. Suppliers of power utilities and power trade services face difficult problems in recognizing and separating out the Disturbance of Power Quality (PQD). This thesis presents a novel approach to identify and classify the power quality disturbances signal based on Singular Spectrum Analysis (SSA) and two different methods of dimension reduction which is Kernel PCA (KPCA) and Principle Component Analysis (PCA) with a recommended Classifier for Classification which is the K-Nearest Neighbors (K-NN) . Total of 16 PQDs waveform is designed on MATLAB using the mathematical model as defined by customary IEEE 1159 and IEC61000 specification and parameters. SSA is a non-parametric technique, does not require any supposition to generate the observed signal, and provides an effective way to decompose and understand the PQ signal. In this thesis, my dataset consists of 16000 generated signals of all 16 types of PQD which is sated in the Power quality Names list, which is then divided into 70% for training and 30% for testing sets for each PQDs. The main objective of this thesis is to improve the accuracy of the K-NN classifier after applying dimension reduction technique. Different tests were carried out by teaching the classifier to analyze and compare the results. The performance outputs varied in reduction of dimension between the classifier k-NN. The dimensionally decreased classifier k-NN succeeds in classifying the Power Quality Disturbance with satisfying performance in both training and testing sets. 2023-05-03T16:29:05Z 2023-05-03T16:29:05Z 2020-02 https://irepository.uniten.edu.my/handle/123456789/21294 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 singular spectrum analysis
power quality disturbances
machines learning
spellingShingle singular spectrum analysis
power quality disturbances
machines learning
Kavines a/l Murugesu
Effect of Signal Decomposition in Power Quality Disturbances Classification
description FYP 2 SEM 2 2019/2020
format
author Kavines a/l Murugesu
author_facet Kavines a/l Murugesu
author_sort Kavines a/l Murugesu
title Effect of Signal Decomposition in Power Quality Disturbances Classification
title_short Effect of Signal Decomposition in Power Quality Disturbances Classification
title_full Effect of Signal Decomposition in Power Quality Disturbances Classification
title_fullStr Effect of Signal Decomposition in Power Quality Disturbances Classification
title_full_unstemmed Effect of Signal Decomposition in Power Quality Disturbances Classification
title_sort effect of signal decomposition in power quality disturbances classification
publishDate 2023
_version_ 1806426067322273792
score 13.214268

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