{"title":"基于环境信号盲源分离的低频振荡模态识别","authors":"W. B. Lin, T. Ji, M. S. Li, Q. Wu","doi":"10.1109/DEMPED.2019.8864902","DOIUrl":null,"url":null,"abstract":"In this paper, a new method is proposed using blind source separation (BSS) and random decrement technique (RDT) for low frequency sscillation (LFO) parameter estimation. The proposed method identifies LFO parameters using ambient data collected from phasor measurement unit (PMU) measurements. Simulation studies are carried out with numerical signals simulated from transfer function and WSCC three-machine nine-bus system. The results indicate that the proposed method can effectively identify LFO paremeters with high accuracy.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low Frequency Oscillation Mode Identification Based On Blind Source Separation Via Ambient Signals\",\"authors\":\"W. B. Lin, T. Ji, M. S. Li, Q. Wu\",\"doi\":\"10.1109/DEMPED.2019.8864902\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new method is proposed using blind source separation (BSS) and random decrement technique (RDT) for low frequency sscillation (LFO) parameter estimation. The proposed method identifies LFO parameters using ambient data collected from phasor measurement unit (PMU) measurements. Simulation studies are carried out with numerical signals simulated from transfer function and WSCC three-machine nine-bus system. The results indicate that the proposed method can effectively identify LFO paremeters with high accuracy.\",\"PeriodicalId\":397001,\"journal\":{\"name\":\"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEMPED.2019.8864902\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEMPED.2019.8864902","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Low Frequency Oscillation Mode Identification Based On Blind Source Separation Via Ambient Signals
In this paper, a new method is proposed using blind source separation (BSS) and random decrement technique (RDT) for low frequency sscillation (LFO) parameter estimation. The proposed method identifies LFO parameters using ambient data collected from phasor measurement unit (PMU) measurements. Simulation studies are carried out with numerical signals simulated from transfer function and WSCC three-machine nine-bus system. The results indicate that the proposed method can effectively identify LFO paremeters with high accuracy.