{"title":"电流推算法中不良数据的可检测性和可识别性分析","authors":"Zhang Na, Yang Gang Wang Dawe","doi":"10.1109/ICACI.2019.8778481","DOIUrl":null,"url":null,"abstract":"In this paper, the observability analysis of the state estimation based on the current reckoning method is carried out, and the relevant conditions for achieving observability are obtained. On this basis, the detectability and identifiability of the bad data in system measurement are analyzed.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detectability and Identifiability Analysis of Bad Data in Current Reckoning Method\",\"authors\":\"Zhang Na, Yang Gang Wang Dawe\",\"doi\":\"10.1109/ICACI.2019.8778481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the observability analysis of the state estimation based on the current reckoning method is carried out, and the relevant conditions for achieving observability are obtained. On this basis, the detectability and identifiability of the bad data in system measurement are analyzed.\",\"PeriodicalId\":213368,\"journal\":{\"name\":\"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACI.2019.8778481\",\"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 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACI.2019.8778481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detectability and Identifiability Analysis of Bad Data in Current Reckoning Method
In this paper, the observability analysis of the state estimation based on the current reckoning method is carried out, and the relevant conditions for achieving observability are obtained. On this basis, the detectability and identifiability of the bad data in system measurement are analyzed.