Modelling of transmission line faults using machine learning methods

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dc.contributor.authorMakgai, Maphale Lucas
dc.contributor.co-supervisorSutherland, G., Dr.
dc.contributor.supervisorJoubert, A., Prof.
dc.date.accessioned2024-07-04T08:05:32Z
dc.date.available2024-07-04T08:05:32Z
dc.date.issued2023-03-30
dc.descriptionM. Eng. (Department of Energy Efficiency, Faculty of Engineering and Technology), Vaal University of Technology.
dc.description.abstractElectrical transmission lines are a link between the generation station and the consumer. Any abnormality in transmission lines may result in a prolonged total electricity shutdown if not detected and identified accurately. Malfunctioning of transmission lines contributes to economic destruction and leads to wildfires and damage to electrical equipment. The existing literature has confirmed that 70 to 80 percent of the abnormalities that usually occur in transmission lines are single line-to-ground (L-G) faults. Detecting and identifying such faults will minimise restoration time. Machine learning-based models' accuracy has never been comprehensively tested in detecting and identifying L-G faults in transmission lines under noisy conditions. This study tested a MATLAB machine learning-based model to detect and identify L-G faults when introducing noise on these transmission lines.
dc.identifier.urihttps://hdl.handle.net/10352/746
dc.language.isoen
dc.publisherVaal University of Technology
dc.subjectElectrical transmission linesen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectElectrical faultsen_US
dc.subjectElectrical power systemen_US
dc.subjectElectric generationen_US
dc.subjectElectric transmissionen_US
dc.subjectElectric distributionen_US
dc.subject.lcshDissertations, Academic -- South Africa.en_US
dc.subject.lcshMATLAB.en_US
dc.subject.lcshData transmission systems.en_US
dc.subject.lcshElectric lines -- Carrier transmission.en_US
dc.subject.lcshSIMULINK.en_US
dc.titleModelling of transmission line faults using machine learning methods
dc.typeThesis
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