{"title":"电能质量监测的数据清洗","authors":"Zijing Yang, Junwei Cao, Yanxiang Xu, Huaying Zhang, Peng Yu, Senjing Yao","doi":"10.1109/ICNDC.2013.39","DOIUrl":null,"url":null,"abstract":"Power quality issues are becoming more critical for high-tech enterprises and grid companies. Many power quality monitoring systems are deployed in recent years. Advanced analysis of monitoring data is not widely applied due to the lackness of data management. In this work, data cleaning technology is introduced to enable advanced study of power quality data, with detailed procedures and software implementation. With power quality monitoring data from Shenzhen Power Supply Company, the effectiveness of data cleaning technology applied for power quality data analysis is demonstrated. Cleaned data that avoid voidness and lackness is more feasible in actual usage, as a good basis for further advanced analysis of power quality.","PeriodicalId":152234,"journal":{"name":"2013 Fourth International Conference on Networking and Distributed Computing","volume":"176 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Data Cleaning for Power Quality Monitoring\",\"authors\":\"Zijing Yang, Junwei Cao, Yanxiang Xu, Huaying Zhang, Peng Yu, Senjing Yao\",\"doi\":\"10.1109/ICNDC.2013.39\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Power quality issues are becoming more critical for high-tech enterprises and grid companies. Many power quality monitoring systems are deployed in recent years. Advanced analysis of monitoring data is not widely applied due to the lackness of data management. In this work, data cleaning technology is introduced to enable advanced study of power quality data, with detailed procedures and software implementation. With power quality monitoring data from Shenzhen Power Supply Company, the effectiveness of data cleaning technology applied for power quality data analysis is demonstrated. Cleaned data that avoid voidness and lackness is more feasible in actual usage, as a good basis for further advanced analysis of power quality.\",\"PeriodicalId\":152234,\"journal\":{\"name\":\"2013 Fourth International Conference on Networking and Distributed Computing\",\"volume\":\"176 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Fourth International Conference on Networking and Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNDC.2013.39\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth International Conference on Networking and Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNDC.2013.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Power quality issues are becoming more critical for high-tech enterprises and grid companies. Many power quality monitoring systems are deployed in recent years. Advanced analysis of monitoring data is not widely applied due to the lackness of data management. In this work, data cleaning technology is introduced to enable advanced study of power quality data, with detailed procedures and software implementation. With power quality monitoring data from Shenzhen Power Supply Company, the effectiveness of data cleaning technology applied for power quality data analysis is demonstrated. Cleaned data that avoid voidness and lackness is more feasible in actual usage, as a good basis for further advanced analysis of power quality.
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