{"title":"扩展卡尔曼滤波与粒子滤波在电力系统动态估计中的应用","authors":"H. Cevallos, Gabriel Intriago, D. Plaza","doi":"10.1109/ETCM.2018.8580285","DOIUrl":null,"url":null,"abstract":"The state estimation (SE) and the load flow (LF) are critical subjects in the analysis and management of electrical power systems (EPS). This article provides a solution for dynamic state estimation (DSE) in EPS based in the Particle Filter (PF) and the Extended Kalman Filter (EKF) that uses the Holt method to linearize the process model. The performance of the methods was analyzed through the comparison of the results considering the error index (ε). In this work, an IEEE 14 and 30 buses test cases were utilized. The results show that the PF method has better accuracy than the EKF method.","PeriodicalId":334574,"journal":{"name":"2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Extended Kalman Filter and the Particle Filter in the Dynamic State Estimation of Electrical Power Systems\",\"authors\":\"H. Cevallos, Gabriel Intriago, D. Plaza\",\"doi\":\"10.1109/ETCM.2018.8580285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The state estimation (SE) and the load flow (LF) are critical subjects in the analysis and management of electrical power systems (EPS). This article provides a solution for dynamic state estimation (DSE) in EPS based in the Particle Filter (PF) and the Extended Kalman Filter (EKF) that uses the Holt method to linearize the process model. The performance of the methods was analyzed through the comparison of the results considering the error index (ε). In this work, an IEEE 14 and 30 buses test cases were utilized. The results show that the PF method has better accuracy than the EKF method.\",\"PeriodicalId\":334574,\"journal\":{\"name\":\"2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETCM.2018.8580285\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETCM.2018.8580285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Extended Kalman Filter and the Particle Filter in the Dynamic State Estimation of Electrical Power Systems
The state estimation (SE) and the load flow (LF) are critical subjects in the analysis and management of electrical power systems (EPS). This article provides a solution for dynamic state estimation (DSE) in EPS based in the Particle Filter (PF) and the Extended Kalman Filter (EKF) that uses the Holt method to linearize the process model. The performance of the methods was analyzed through the comparison of the results considering the error index (ε). In this work, an IEEE 14 and 30 buses test cases were utilized. The results show that the PF method has better accuracy than the EKF method.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
