{"title":"基于并行神经模糊技术的输电线路故障检测与定位","authors":"P. Eboule, J. Pretorius, N. Mbuli, Collins Leke","doi":"10.1109/EPEC.2018.8598311","DOIUrl":null,"url":null,"abstract":"In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"708 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique\",\"authors\":\"P. Eboule, J. Pretorius, N. Mbuli, Collins Leke\",\"doi\":\"10.1109/EPEC.2018.8598311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.\",\"PeriodicalId\":265297,\"journal\":{\"name\":\"2018 IEEE Electrical Power and Energy Conference (EPEC)\",\"volume\":\"708 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Electrical Power and Energy Conference (EPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EPEC.2018.8598311\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Electrical Power and Energy Conference (EPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPEC.2018.8598311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique
In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.
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