{"title":"互信息拓扑估计中测量值的选择","authors":"J. Krstulovic, Vladimiro Miranda","doi":"10.1109/ENERGYCON.2014.6850486","DOIUrl":null,"url":null,"abstract":"This paper discusses mechanisms for establishing an efficient decentralized methodology for the reconstruction of topology in power systems. The maximum mutual information criterion is proposed as a selection criterion for the inputs of a distributed topology estimator, based on mosaic of local auto-associative neural networks. The proposed concepts offer some strong theoretical support for an information theoretic perspective on power system state estimation. The results are confirmed by extensive tests conducted on the IEEE RTS 24-bus system.","PeriodicalId":410611,"journal":{"name":"2014 IEEE International Energy Conference (ENERGYCON)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Selection of measurements in topology estimation with mutual information\",\"authors\":\"J. Krstulovic, Vladimiro Miranda\",\"doi\":\"10.1109/ENERGYCON.2014.6850486\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses mechanisms for establishing an efficient decentralized methodology for the reconstruction of topology in power systems. The maximum mutual information criterion is proposed as a selection criterion for the inputs of a distributed topology estimator, based on mosaic of local auto-associative neural networks. The proposed concepts offer some strong theoretical support for an information theoretic perspective on power system state estimation. The results are confirmed by extensive tests conducted on the IEEE RTS 24-bus system.\",\"PeriodicalId\":410611,\"journal\":{\"name\":\"2014 IEEE International Energy Conference (ENERGYCON)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Energy Conference (ENERGYCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ENERGYCON.2014.6850486\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Energy Conference (ENERGYCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ENERGYCON.2014.6850486","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Selection of measurements in topology estimation with mutual information
This paper discusses mechanisms for establishing an efficient decentralized methodology for the reconstruction of topology in power systems. The maximum mutual information criterion is proposed as a selection criterion for the inputs of a distributed topology estimator, based on mosaic of local auto-associative neural networks. The proposed concepts offer some strong theoretical support for an information theoretic perspective on power system state estimation. The results are confirmed by extensive tests conducted on the IEEE RTS 24-bus system.
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