Automatic contingency grouping using partial least squares and feed forward neural network technologies applied to the static security assessment problem
{"title":"Automatic contingency grouping using partial least squares and feed forward neural network technologies applied to the static security assessment problem","authors":"D. Fischer, B. Szabados, S. Poehlman","doi":"10.1109/LESCPE.2003.1204684","DOIUrl":null,"url":null,"abstract":"The paper shows how a number of feed forward back propagation neural networks can be trained to predict power system bus voltages after a contingency. The approach is designed to use very few learning examples. thus being suitable for on-line use. The method was applied to the 10-machine, 39-bus New England Power System model.","PeriodicalId":226571,"journal":{"name":"Large Engineering Systems Conference on Power Engineering, 2003","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Large Engineering Systems Conference on Power Engineering, 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LESCPE.2003.1204684","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
The paper shows how a number of feed forward back propagation neural networks can be trained to predict power system bus voltages after a contingency. The approach is designed to use very few learning examples. thus being suitable for on-line use. The method was applied to the 10-machine, 39-bus New England Power System model.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
