{"title":"人工智能有助于减少变压器铁的损耗","authors":"P. Georgilakis, N. Hatziargyriou, D. Paparigas","doi":"10.1109/67.795137","DOIUrl":null,"url":null,"abstract":"Methods for iron loss reduction during manufacturing of wound-core distribution transformers are presented. More specifically, measurements taken at the first stages of core construction are effectively used, in order to minimize iron losses of transformer (final product). To optimally exploit the measurements (feedback), artificial intelligence methods are applied. It is shown that intelligent systems are able to learn and interpret several variations of the same conditions, thus helping in predicting iron losses with increased accuracy.","PeriodicalId":435675,"journal":{"name":"IEEE Computer Applications in Power","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"60","resultStr":"{\"title\":\"AI helps reduce transformer iron losses\",\"authors\":\"P. Georgilakis, N. Hatziargyriou, D. Paparigas\",\"doi\":\"10.1109/67.795137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Methods for iron loss reduction during manufacturing of wound-core distribution transformers are presented. More specifically, measurements taken at the first stages of core construction are effectively used, in order to minimize iron losses of transformer (final product). To optimally exploit the measurements (feedback), artificial intelligence methods are applied. It is shown that intelligent systems are able to learn and interpret several variations of the same conditions, thus helping in predicting iron losses with increased accuracy.\",\"PeriodicalId\":435675,\"journal\":{\"name\":\"IEEE Computer Applications in Power\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"60\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Computer Applications in Power\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/67.795137\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Applications in Power","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/67.795137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Methods for iron loss reduction during manufacturing of wound-core distribution transformers are presented. More specifically, measurements taken at the first stages of core construction are effectively used, in order to minimize iron losses of transformer (final product). To optimally exploit the measurements (feedback), artificial intelligence methods are applied. It is shown that intelligent systems are able to learn and interpret several variations of the same conditions, thus helping in predicting iron losses with increased accuracy.