The characterisation and automatic classification of transmission line faults

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dc.contributor.advisorGaunt, C Ten_ZA
dc.contributor.advisorNicolls, Freden_ZA
dc.contributor.authorMinnaar, Ulrichen_ZA
dc.date.accessioned2014-11-07T09:04:23Z
dc.date.available2014-11-07T09:04:23Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractA country's ability to sustain and grow its industrial and commercial activities is highly dependent on a reliable electricity supply. Electrical faults on transmission lines are a cause of both interruptions to supply and voltage dips. These are the most common events impacting electricity users and also have the largest financial impact on them. This research focuses on understanding the causes of transmission line faults and developing methods to automatically identify these causes. Records of faults occurring on the South African power transmission system over a 16-year period have been collected and analysed to find statistical relationships between local climate, key design parameters of the overhead lines and the main causes of power system faults. The results characterize the performance of the South African transmission system on a probabilistic basis and illustrate differences in fault cause statistics for the summer and winter rainfall areas of South Africa and for different times of the year and day. This analysis lays a foundation for reliability analysis and fault pattern recognition taking environmental features such as local geography, climate and power system parameters into account. A key aspect of using pattern recognition techniques is selecting appropriate classifying features. Transmission line fault waveforms are characterised by instantaneous symmetrical component analysis to describe the transient and steady state fault conditions. The waveform and environmental features are used to develop single nearest neighbour classifiers to identify the underlying cause of transmission line faults. A classification accuracy of 86% is achieved using a single nearest neighbour classifier. This classification performance is found to be superior to that of decision tree, artificial neural network and naïve Bayes classifiers. The results achieved demonstrate that transmission line faults can be automatically classified according to cause.en_ZA
dc.identifier.apacitationMinnaar, U. (2014). <i>The characterisation and automatic classification of transmission line faults</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/9287en_ZA
dc.identifier.chicagocitationMinnaar, Ulrich. <i>"The characterisation and automatic classification of transmission line faults."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014. http://hdl.handle.net/11427/9287en_ZA
dc.identifier.citationMinnaar, U. 2014. The characterisation and automatic classification of transmission line faults. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Minnaar, Ulrich AB - A country's ability to sustain and grow its industrial and commercial activities is highly dependent on a reliable electricity supply. Electrical faults on transmission lines are a cause of both interruptions to supply and voltage dips. These are the most common events impacting electricity users and also have the largest financial impact on them. This research focuses on understanding the causes of transmission line faults and developing methods to automatically identify these causes. Records of faults occurring on the South African power transmission system over a 16-year period have been collected and analysed to find statistical relationships between local climate, key design parameters of the overhead lines and the main causes of power system faults. The results characterize the performance of the South African transmission system on a probabilistic basis and illustrate differences in fault cause statistics for the summer and winter rainfall areas of South Africa and for different times of the year and day. This analysis lays a foundation for reliability analysis and fault pattern recognition taking environmental features such as local geography, climate and power system parameters into account. A key aspect of using pattern recognition techniques is selecting appropriate classifying features. Transmission line fault waveforms are characterised by instantaneous symmetrical component analysis to describe the transient and steady state fault conditions. The waveform and environmental features are used to develop single nearest neighbour classifiers to identify the underlying cause of transmission line faults. A classification accuracy of 86% is achieved using a single nearest neighbour classifier. This classification performance is found to be superior to that of decision tree, artificial neural network and naïve Bayes classifiers. The results achieved demonstrate that transmission line faults can be automatically classified according to cause. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - The characterisation and automatic classification of transmission line faults TI - The characterisation and automatic classification of transmission line faults UR - http://hdl.handle.net/11427/9287 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9287
dc.identifier.vancouvercitationMinnaar U. The characterisation and automatic classification of transmission line faults. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9287en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.titleThe characterisation and automatic classification of transmission line faultsen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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