{"title":"Load Profile Identification using Independent Component Analysis","authors":"E. Bobric, D. Irimia","doi":"10.1109/SIELMEN.2019.8905826","DOIUrl":null,"url":null,"abstract":"The paper aims to establish the load profiles for the consumers, in a looped network, when the power circuits are known on the branches The algorithm used is independent component analysis (ICA), a statistic computational method that allows the extraction of additive components from a mixed signal. The method requires as restriction the statistically independence of the additive components. In the paper, ICA was used to reconstruct some statistically independent signals (power requests according to consumer load profiles) from a mixture of signals (power flows on branches). The ICA algorithm does not require knowledge of network parameters or configuration. The consumer load profiles obtained by the independent component method overlapped with a very good approximation over the real load graphs of consumers used in modeling.","PeriodicalId":129030,"journal":{"name":"2019 International Conference on Electromechanical and Energy Systems (SIELMEN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Electromechanical and Energy Systems (SIELMEN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIELMEN.2019.8905826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The paper aims to establish the load profiles for the consumers, in a looped network, when the power circuits are known on the branches The algorithm used is independent component analysis (ICA), a statistic computational method that allows the extraction of additive components from a mixed signal. The method requires as restriction the statistically independence of the additive components. In the paper, ICA was used to reconstruct some statistically independent signals (power requests according to consumer load profiles) from a mixture of signals (power flows on branches). The ICA algorithm does not require knowledge of network parameters or configuration. The consumer load profiles obtained by the independent component method overlapped with a very good approximation over the real load graphs of consumers used in modeling.