{"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}
引用次数: 5
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.