{"title":"基于神经网络的远端电源中压配电线路故障定位","authors":"Y. Aslan, Y. E. Yağan","doi":"10.1109/ISFEE.2016.7803203","DOIUrl":null,"url":null,"abstract":"This study presents a fault location algorithm for medium voltage (MV) overhead power distribution lines based on artificial neural network (ANN). In the study the possibility of connection of a small scale remote-end source connection to the end of a radial distribution feeder has been considered. In the study, feed forward ANN with back propagation algorithm with Levenberg-Marquardt training function is used. The ANN inputs were formed by using frequency information of fault data which were obtained with digital filtering. The algorithm is extensively tested for a various system conditions for the faults created on the overhead distribution system which has been modeled with Matlab/Simulink software. From the results attained it is seen that the proposed technique has not been significantly affected from the connection of a small scale hydroelectric generator to the existing distribution system.","PeriodicalId":240170,"journal":{"name":"2016 International Symposium on Fundamentals of Electrical Engineering (ISFEE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"ANN based fault location for medium voltage distribution lines with remote-end source\",\"authors\":\"Y. Aslan, Y. E. Yağan\",\"doi\":\"10.1109/ISFEE.2016.7803203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents a fault location algorithm for medium voltage (MV) overhead power distribution lines based on artificial neural network (ANN). In the study the possibility of connection of a small scale remote-end source connection to the end of a radial distribution feeder has been considered. In the study, feed forward ANN with back propagation algorithm with Levenberg-Marquardt training function is used. The ANN inputs were formed by using frequency information of fault data which were obtained with digital filtering. The algorithm is extensively tested for a various system conditions for the faults created on the overhead distribution system which has been modeled with Matlab/Simulink software. From the results attained it is seen that the proposed technique has not been significantly affected from the connection of a small scale hydroelectric generator to the existing distribution system.\",\"PeriodicalId\":240170,\"journal\":{\"name\":\"2016 International Symposium on Fundamentals of Electrical Engineering (ISFEE)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Symposium on Fundamentals of Electrical Engineering (ISFEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISFEE.2016.7803203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Symposium on Fundamentals of Electrical Engineering (ISFEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISFEE.2016.7803203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ANN based fault location for medium voltage distribution lines with remote-end source
This study presents a fault location algorithm for medium voltage (MV) overhead power distribution lines based on artificial neural network (ANN). In the study the possibility of connection of a small scale remote-end source connection to the end of a radial distribution feeder has been considered. In the study, feed forward ANN with back propagation algorithm with Levenberg-Marquardt training function is used. The ANN inputs were formed by using frequency information of fault data which were obtained with digital filtering. The algorithm is extensively tested for a various system conditions for the faults created on the overhead distribution system which has been modeled with Matlab/Simulink software. From the results attained it is seen that the proposed technique has not been significantly affected from the connection of a small scale hydroelectric generator to the existing distribution system.