{"title":"数据驱动的配电系统故障风险预警方法","authors":"Yuhui Huang, Pu Zhao, Yi Jiang","doi":"10.1109/CICED50259.2021.9556654","DOIUrl":null,"url":null,"abstract":"A data-driven method for fault risk prediction of distribution network is proposed. Firstly, data analysis are carried out to determine the target classification and the correlation feature set of the distribution network failure. The Adaboost-SVM algorithm is presented to forecast the distribution network faults and excavates the relationship between the failure and the influencing factors. The calculation results show that the fault warning method is effective and provide significant reference to the operation practice.","PeriodicalId":221387,"journal":{"name":"2021 China International Conference on Electricity Distribution (CICED)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Data-driven fault risk warning method for distribution system\",\"authors\":\"Yuhui Huang, Pu Zhao, Yi Jiang\",\"doi\":\"10.1109/CICED50259.2021.9556654\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A data-driven method for fault risk prediction of distribution network is proposed. Firstly, data analysis are carried out to determine the target classification and the correlation feature set of the distribution network failure. The Adaboost-SVM algorithm is presented to forecast the distribution network faults and excavates the relationship between the failure and the influencing factors. The calculation results show that the fault warning method is effective and provide significant reference to the operation practice.\",\"PeriodicalId\":221387,\"journal\":{\"name\":\"2021 China International Conference on Electricity Distribution (CICED)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 China International Conference on Electricity Distribution (CICED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICED50259.2021.9556654\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 China International Conference on Electricity Distribution (CICED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICED50259.2021.9556654","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data-driven fault risk warning method for distribution system
A data-driven method for fault risk prediction of distribution network is proposed. Firstly, data analysis are carried out to determine the target classification and the correlation feature set of the distribution network failure. The Adaboost-SVM algorithm is presented to forecast the distribution network faults and excavates the relationship between the failure and the influencing factors. The calculation results show that the fault warning method is effective and provide significant reference to the operation practice.
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