<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T00:18:31Z</responseDate><request verb="GetRecord" identifier="oai:digital.library.adelaide.edu.au:2440/63561" metadataPrefix="dim">https://digital.library.adelaide.edu.au/server/oai/request</request><GetRecord><record><header><identifier>oai:digital.library.adelaide.edu.au:2440/63561</identifier><datestamp>2015-09-11T03:05:18Z</datestamp><setSpec>com_2440_14759</setSpec><setSpec>col_2440_14760</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en">Gargoom, Ameen M.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="school" lang="en">School of Electrical and Electronic Engineering</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en">2007</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/2440/63561</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en">The work presented in this thesis investigates the application of digital signal processing techniques in the power quality automatic classification field, and thus, proposes an optimized automatic monitoring system with an improved accuracy. The proposed monitoring system involves three main sections: detection of the power quality events, extraction of the distinctive features that characterise each event, and automatic classification of the similar events under pre-defined categories. The thesis proposes new power quality processing techniques for detection and feature extraction sections, including the Hilbert and Clarke transforms. The former proposed technique was used for analysing single phase signals, while the later technique was proposed for the simultaneous analysis of three signals.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="dissertation" lang="en">Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2007</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en">signal processing digital techniques; electricity.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en">Digital signal processing techniques for improving the automatic classification of power quality events.</dim:field>
   <dim:field mdschema="dc" element="type" lang="en">Thesis</dim:field>
   <dim:field mdschema="dc" element="provenance" lang="en">This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exception. If you are the author of this thesis and do not wish it to be made publicly available or If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals.</dim:field>open.access</dim:dim></metadata></record></GetRecord></OAI-PMH>