M. Shafiq, I. Kiitam, P. Taklaja, Amjad Hussain, L. Kütt, K. Kauhaniemi
{"title":"Characterization of Corona and Internal Partial Discharge under Increasing Electrical Stress using Time Domain Analysis","authors":"M. Shafiq, I. Kiitam, P. Taklaja, Amjad Hussain, L. Kütt, K. Kauhaniemi","doi":"10.1109/eic47619.2020.9158701","DOIUrl":null,"url":null,"abstract":"Aging and abnormal stresses accelerate insulation degradation and reduce the lifetime of power equipment. Partial discharge (PD) measurement is an effective tool to study the condition of the insulation. Reliability of PD diagnosis depends on the accurate interpretation of the measured PD signals. PD itself is a complex phenomenon and the presence of different types of discharge sources makes interpretation of the PD data quite challenging. This paper investigates internal and corona PDs in order to distinguish them when both are active simultaneously. The presented work identifies the PD sources based on time domain analysis that provides a simplified solution as compared to identification techniques based on different statistical features leading to complex data processing. While superimposed phase-resolved partial discharge (PRPD) patterns provided incomplete information, time domain PD characteristics e.g. pulse repetition rate and pulse amplitude combined with PRPD mapping are analyzed to differentiate PD activity. Furthermore, the electrical stress (voltage level) is increased gradually in the experiments made and PD behavior is studied. The presented technique contributes to enhancing the accuracy of PD diagnosis that is necessary for appropriate decision making concerning the repair of affected components.","PeriodicalId":286019,"journal":{"name":"2020 IEEE Electrical Insulation Conference (EIC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Electrical Insulation Conference (EIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eic47619.2020.9158701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Aging and abnormal stresses accelerate insulation degradation and reduce the lifetime of power equipment. Partial discharge (PD) measurement is an effective tool to study the condition of the insulation. Reliability of PD diagnosis depends on the accurate interpretation of the measured PD signals. PD itself is a complex phenomenon and the presence of different types of discharge sources makes interpretation of the PD data quite challenging. This paper investigates internal and corona PDs in order to distinguish them when both are active simultaneously. The presented work identifies the PD sources based on time domain analysis that provides a simplified solution as compared to identification techniques based on different statistical features leading to complex data processing. While superimposed phase-resolved partial discharge (PRPD) patterns provided incomplete information, time domain PD characteristics e.g. pulse repetition rate and pulse amplitude combined with PRPD mapping are analyzed to differentiate PD activity. Furthermore, the electrical stress (voltage level) is increased gradually in the experiments made and PD behavior is studied. The presented technique contributes to enhancing the accuracy of PD diagnosis that is necessary for appropriate decision making concerning the repair of affected components.