Fault detection on power cables based on ultrasound images and fourth-order cumulants

dc.contributor.authorZhang, Huixin
dc.contributor.examiningcommitteeAlfa, Attahiru S. (Electrical and Computer Engineering) Birouk, Madjid (Mechanical Engineering)en_US
dc.contributor.supervisorThomas, Gabriel (Electrical and Computer Engineering)en_US
dc.date.accessioned2016-02-10T21:30:32Z
dc.date.available2016-02-10T21:30:32Z
dc.date.issued2016
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractElectrical power transmission companies have been inspecting underground power cables in a time consuming and destructive way. The current methodology used by Manitoba Hydro, is to remove the conductive material in the center of the cable, cutting the cable into wafers leaving behind the insulating polymer material known as XLPE, the area where many faults occur, and inspect the wafers manually with a microscope. The main goal of this work was to find a methodology to detect these cable faults in a non-destructive way so that the quality of the cable may be assessed, and its remaining lifetime be estimated and return it to use if possible. Two XLPE power cable samples were tested. Three small holes were drilled in one XLPE cable. A capacitive transducer with center frequency of 802.8 kHz was applied for transmitting receiving signal. For each sample, 48 scans were collected. Based on ultrasound images, we were able to detect these faults in this XLPE material from the peaks of the samples corresponding to the XLPE area by setting a threshold to 0.08 volts. Also, this detection technique was improved by using fourth-order cumulants.en_US
dc.description.noteMay 2016en_US
dc.identifier.urihttp://hdl.handle.net/1993/31132
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectXLPE Power Cable, Capacitive transducer, water tree, higher order spectraen_US
dc.titleFault detection on power cables based on ultrasound images and fourth-order cumulantsen_US
dc.typemaster thesisen_US
local.subject.manitobayesen_US
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