Pub Date : 1900-01-01DOI: 10.1109/PECON.2003.1437420
N. H. Hashim, T. Rahman, M. Latip, I. Musirin
This paper presents an artificial neural network (ANN) technique for determining optimum tapping ratio of tap changing transformer, which will in turn minimise real power losses in an electrical power system. Training data containing variety of load patterns, tap changing ratio and real power losses associated with each tapping, are fed into a neural network. By using the Levenberg-Marquardt algorithm, a back propagation network is trained so that it can predict the optimum tap ratio when unseen data are fed into the network. The technique was tested on a 6-bus IEEE system and the results show that the proposed ANN technique is highly accurate, reliable and capable to predict at a faster rate.
{"title":"Application of ANN to determine the OLTC in minimizing the real power losses in a power system","authors":"N. H. Hashim, T. Rahman, M. Latip, I. Musirin","doi":"10.1109/PECON.2003.1437420","DOIUrl":"https://doi.org/10.1109/PECON.2003.1437420","url":null,"abstract":"This paper presents an artificial neural network (ANN) technique for determining optimum tapping ratio of tap changing transformer, which will in turn minimise real power losses in an electrical power system. Training data containing variety of load patterns, tap changing ratio and real power losses associated with each tapping, are fed into a neural network. By using the Levenberg-Marquardt algorithm, a back propagation network is trained so that it can predict the optimum tap ratio when unseen data are fed into the network. The technique was tested on a 6-bus IEEE system and the results show that the proposed ANN technique is highly accurate, reliable and capable to predict at a faster rate.","PeriodicalId":136640,"journal":{"name":"Proceedings. National Power Engineering Conference, 2003. PECon 2003.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132688858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：