Ibrahim Al-Omari, A. Rahimnejad, S. Gadsden, M. Moussa, H. Karimipour
{"title":"基于光滑变结构滤波器的电力系统动态估计","authors":"Ibrahim Al-Omari, A. Rahimnejad, S. Gadsden, M. Moussa, H. Karimipour","doi":"10.1109/GlobalSIP45357.2019.8969306","DOIUrl":null,"url":null,"abstract":"With the integration of distributed energy resources (DER) traditional power systems evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on real-time monitoring and control of the power grid. State estimation is a key function which plays a vital role in reliable system control. In this paper, the smooth variable structure filter (SVSF) is used for power system dynamic state estimation (DSE). SVSF is a predictor-corrector based approach which can be applied to both linear and nonlinear system with the ability to deal with the system uncertainties. The simulation results on a single machine with infinite bus power network shows the superiority of the proposed SVSF compared to extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of the proposed method show a significant smoothness and accuracy in its performance compared to those obtained from EKF and UKF approaches; in particular, in the presence of measurement outliers.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Power System Dynamic State Estimation Using Smooth Variable Structure Filter\",\"authors\":\"Ibrahim Al-Omari, A. Rahimnejad, S. Gadsden, M. Moussa, H. Karimipour\",\"doi\":\"10.1109/GlobalSIP45357.2019.8969306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the integration of distributed energy resources (DER) traditional power systems evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on real-time monitoring and control of the power grid. State estimation is a key function which plays a vital role in reliable system control. In this paper, the smooth variable structure filter (SVSF) is used for power system dynamic state estimation (DSE). SVSF is a predictor-corrector based approach which can be applied to both linear and nonlinear system with the ability to deal with the system uncertainties. The simulation results on a single machine with infinite bus power network shows the superiority of the proposed SVSF compared to extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of the proposed method show a significant smoothness and accuracy in its performance compared to those obtained from EKF and UKF approaches; in particular, in the presence of measurement outliers.\",\"PeriodicalId\":221378,\"journal\":{\"name\":\"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)\",\"volume\":\"134 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GlobalSIP45357.2019.8969306\",\"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 Global Conference on Signal and Information Processing (GlobalSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobalSIP45357.2019.8969306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Power System Dynamic State Estimation Using Smooth Variable Structure Filter
With the integration of distributed energy resources (DER) traditional power systems evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on real-time monitoring and control of the power grid. State estimation is a key function which plays a vital role in reliable system control. In this paper, the smooth variable structure filter (SVSF) is used for power system dynamic state estimation (DSE). SVSF is a predictor-corrector based approach which can be applied to both linear and nonlinear system with the ability to deal with the system uncertainties. The simulation results on a single machine with infinite bus power network shows the superiority of the proposed SVSF compared to extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of the proposed method show a significant smoothness and accuracy in its performance compared to those obtained from EKF and UKF approaches; in particular, in the presence of measurement outliers.