{"title":"变压器频响识别基函数极点位置优化","authors":"Samuel Wolinski, J. Welsh, J. Tusek","doi":"10.1109/AUPEC.2013.6725408","DOIUrl":null,"url":null,"abstract":"In this paper a technique for obtaining accurate parametric models of wideband frequency domain systems is applied to modeling the frequency response of power transformers. Obtaining accurate frequency response models is a problem for conventional identification techniques, and typically results in ill-conditioning due to the frequency range and numerous resonant modes of the transformer. `Frequency Localising Basis Functions' improve the conditioning of the estimator, and hence provide a more accurate fit. The location of these functions are optimized using Particle Swarm Optimization, and applied to frequency response data taken from a power transformer. A case study is undertaken on a 132kV, 60MVA power transformer.","PeriodicalId":121040,"journal":{"name":"2013 Australasian Universities Power Engineering Conference (AUPEC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing basis function pole locations for transformer frequency response identification\",\"authors\":\"Samuel Wolinski, J. Welsh, J. Tusek\",\"doi\":\"10.1109/AUPEC.2013.6725408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a technique for obtaining accurate parametric models of wideband frequency domain systems is applied to modeling the frequency response of power transformers. Obtaining accurate frequency response models is a problem for conventional identification techniques, and typically results in ill-conditioning due to the frequency range and numerous resonant modes of the transformer. `Frequency Localising Basis Functions' improve the conditioning of the estimator, and hence provide a more accurate fit. The location of these functions are optimized using Particle Swarm Optimization, and applied to frequency response data taken from a power transformer. A case study is undertaken on a 132kV, 60MVA power transformer.\",\"PeriodicalId\":121040,\"journal\":{\"name\":\"2013 Australasian Universities Power Engineering Conference (AUPEC)\",\"volume\":\"95 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Australasian Universities Power Engineering Conference (AUPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AUPEC.2013.6725408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Australasian Universities Power Engineering Conference (AUPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AUPEC.2013.6725408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing basis function pole locations for transformer frequency response identification
In this paper a technique for obtaining accurate parametric models of wideband frequency domain systems is applied to modeling the frequency response of power transformers. Obtaining accurate frequency response models is a problem for conventional identification techniques, and typically results in ill-conditioning due to the frequency range and numerous resonant modes of the transformer. `Frequency Localising Basis Functions' improve the conditioning of the estimator, and hence provide a more accurate fit. The location of these functions are optimized using Particle Swarm Optimization, and applied to frequency response data taken from a power transformer. A case study is undertaken on a 132kV, 60MVA power transformer.